U.S. patent application number 10/915059 was filed with the patent office on 2005-05-26 for reagents and methods for use in cancer diagnosis, classification and therapy.
Invention is credited to Ring, Brian Z., Ross, Douglas T., Seitz, Robert S..
Application Number | 20050112622 10/915059 |
Document ID | / |
Family ID | 34198967 |
Filed Date | 2005-05-26 |
United States Patent
Application |
20050112622 |
Kind Code |
A1 |
Ring, Brian Z. ; et
al. |
May 26, 2005 |
Reagents and methods for use in cancer diagnosis, classification
and therapy
Abstract
Methods and reagents for classifying tumors and for identifying
new tumor classes and subclasses. Methods for correlating tumor
class or subclass with therapeutic regimen or outcome, for
identifying appropriate (new or known) therapies for particular
classes or subclasses, and for predicting outcomes based on class
or subclass. New therapeutic agents and methods for the treatment
of cancer.
Inventors: |
Ring, Brian Z.; (Foster
City, CA) ; Ross, Douglas T.; (Burlingame, CA)
; Seitz, Robert S.; (Hampton Cove, AL) |
Correspondence
Address: |
CHOATE, HALL & STEWART LLP
EXCHANGE PLACE
53 STATE STREET
BOSTON
MA
02109
US
|
Family ID: |
34198967 |
Appl. No.: |
10/915059 |
Filed: |
August 10, 2004 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
60494334 |
Aug 11, 2003 |
|
|
|
60570206 |
May 12, 2004 |
|
|
|
Current U.S.
Class: |
435/6.14 ;
435/7.23 |
Current CPC
Class: |
G01N 2500/04 20130101;
G01N 33/57484 20130101; G01N 2800/52 20130101; G01N 33/6893
20130101 |
Class at
Publication: |
435/006 ;
435/007.23 |
International
Class: |
C12Q 001/68; G01N
033/574 |
Claims
We claim:
1. A method of identifying interaction partners whose binding to
tumor markers correlates with patient prognosis, the method
comprising steps of: providing a set of potential interaction
partners; and contacting the set of potential interaction partners
with a collection of tumor samples that includes samples of tumors
from patients with different prognosis', so that interaction
partners that bind differentially to the tumor samples are
identified.
2. The method of claim 1, further comprising a step of: defining a
panel of the differentially binding interaction partners whose
collective binding correlates with a particular patient
prognosis.
3. A method of assessing prognosis of a patient having a particular
tumor, the method comprising steps of: obtaining a tumor sample
from a patient with unknown prognosis; contacting the sample with a
panel of interaction partners whose binding has been correlated
with a particular prognosis; and assessing the patient's likely
prognosis based upon binding of the panel to the tumor sample.
4. A method of identifying interaction partners whose binding to
tumor markers correlates with responsiveness to therapy, the method
comprising steps of: providing a set of potential interaction
partners; and contacting the set of potential interaction partners
with a collection of tumor samples that includes samples of tumors
that respond differently to therapy, so that interaction partners
that bind differentially to the tumor samples are identified.
5. The method of claim 4, further comprising a step of: defining a
panel of the differentially binding interaction partners whose
collective binding correlates with a particular response to
therapy.
6. A method of predicting responsiveness of a particular tumor to
therapy, the method comprising steps of: providing a sample from a
tumor of unknown responsiveness; contacting the sample with a panel
of interaction partners whose binding has been correlated with a
particular response to therapy; and assessing the tumor's likely
responsiveness to therapy based on binding by the panel to the
tumor sample.
7. A method of identifying interaction partners whose binding to
tumor markers correlates with tumor class or subclass, the method
comprising steps of: providing a set of potential interaction
partners; contacting the set of interaction partners with a
collection of tumor samples that includes samples from different
tumor classes or subclasses, so that interaction partners that bind
differentially to the tumor samples are identified.
8. The method of claim 7, further comprising: defining a panel of
the differentially binding interaction partners whose collective
binding correlates with a particular tumor class or subclass.
9. A method of classifying a tumor, the method comprising steps of:
providing a sample from the tumor; and contacting the sample with a
panel of interaction partners whose binding has been correlated
with the identity of a particular class or subclass of tumors.
10. The method of claim 1, 4, or 7, wherein certain interaction
partners exhibit differential binding due to specific binding to
polypeptides that are expressed by tumor cells of a particular
tumor class or subclass.
11. The method of claim 1, 4, or 7, wherein certain interaction
partners exhibit differential binding due to lack of binding to
polypeptides that are expressed by tumor cells of a particular
tumor class or subclass.
12. The method of claim 1, 4, or 7, wherein at least one of the
interaction partners is an antibody.
13. The method of claim 12, wherein the antibody is selected from
the group consisting of monoclonal antibodies, polyclonal
antibodies, antibody fragments, chimeric antibodies, and
combinations thereof.
14. The method of claim 1, 4, or 7, wherein the collection of tumor
samples comprises tumor samples from solid tumors.
15. The method of claim 1, 4, or 7, wherein the collection of tumor
samples comprises tumor samples from breast tumors.
16. The method of claim 1, 4, or 7, wherein the collection of tumor
samples comprises tumor samples from lung tumors.
17. The method of claim 1, 4, or 7, wherein the collection of tumor
samples comprises tumor samples from colon tumors.
18. The method of claim 1, 4, or 7, wherein the collection of tumor
samples comprises tumor samples from ovarian tumors.
19. The method of claim 1, 4, or 7, wherein the collection of tumor
samples comprises tumor samples having a common trait selected from
the group consisting of tissue of origin, stage of tumor,
microscopic characteristics, submicroscopic characteristics, and
combinations thereof.
20. The method of claim 2, 5, or 8, wherein the binding correlates
with a p-value of less than 0.05.
21. The method of claim 3, 6, or 9, wherein the interaction
partners are antibodies.
22. The method of claim 21, wherein the antibodies are selected
from the group consisting of monoclonal antibodies, polyclonal
antibodies, antibody fragments, chimeric antibodies, and
combinations thereof.
23. The method of claim 3, 6, or 9, wherein the tumor sample is a
sample of a solid tumor.
24. The method of claim 3, 6, or 9, wherein the tumor sample is a
sample of a breast tumor.
25. The method of claim 3, 6, or 9, wherein the tumor sample is a
sample of a lung tumor.
26. The method of claim 3, 6, or 9, wherein the tumor sample is a
sample of a colon tumor.
27. The method of claim 3, 6, or 9, wherein the tumor sample is a
sample of an ovarian tumor.
28. A kit comprising: a panel of interaction partners whose binding
with tumor samples has been correlated with patient prognosis.
29. A kit comprising: a panel of interaction partners whose binding
with tumor samples has been correlated with responsiveness to
therapy.
30. A kit comprising: a panel of interaction partners whose binding
with tumor samples has been correlated with the identity of a
particular class or subclass of tumors.
31. The kit of claim 28, 29, or 30, wherein the interaction
partners are antibodies.
32. The kit of claim 31, wherein the antibodies are selected from
the group consisting of monoclonal antibodies, polyclonal
antibodies, antibody fragments, chimeric antibodies, and
combinations thereof.
33. A method of identifying an interaction partner that is useful
as a therapeutic agent for the treatment of cancer, the method
comprising steps of: providing a set of interaction partners that
bind specifically with polypeptides expressed by tumor cells;
contacting the set of interaction partners with a collection of
tumor samples; and identifying interaction partners whose binding
inhibits tumor cell growth.
34. A therapeutic agent for the treatment of cancer comprising: at
least one interaction partner that binds specifically with a
polypeptide expressed by tumor cells so that tumor cell growth is
inhibited.
35. The therapeutic agent of claim 34, wherein the interaction
partner is identified according to the method of claim 33.
Description
PRIORITY INFORMATION
[0001] This application claims priority to U.S. Ser. No. 60/494,334
filed Aug. 11, 2003 and U.S. Ser. No. 60/570,206 filed May 12,
2004. The entire contents of both of these priority applications
are hereby incorporated by reference.
BACKGROUND OF THE INVENTION
[0002] A major challenge of cancer treatment is the selection of
therapeutic regimens that maximize efficacy and minimize toxicity
for a given patient. A related challenge lies in the attempt to
provide accurate diagnostic, prognostic and predictive information.
At present, tumors are generally classified under the
tumor-node-metastasis (TNM) system. This system, which uses the
size of the tumor, the presence or absence of tumor in regional
lymph nodes, and the presence or absence of distant metastases, to
assign a stage to the tumor is described in the AJCC Cancer Staging
Manual, Lippincott, 5th ed., pp. 171-180 (1997). The assigned stage
is used as a basis for selection of appropriate therapy and for
prognostic purposes. In addition to the TNM parameters, morphologic
appearance is used to further classify tumors into tumor types and
thereby aid in selection of appropriate therapy. However, this
approach has serious limitations. Tumors with similar
histopathologic appearance can exhibit significant variability in
terms of clinical course and response to therapy. For example, some
tumors are rapidly progressive while others are not. Some tumors
respond readily to hormonal therapy or chemotherapy while others
are resistant.
[0003] Assays for cell surface markers, e.g., using
immunohistochemistry, have provided means for dividing certain
tumor types into subclasses. For example, one factor considered in
prognosis and treatment decisions for breast cancer is the presence
or absence of the estrogen receptor (ER) in tumor samples.
ER-positive breast cancers typically respond much more readily to
hormonal therapies such as tamoxifen, which acts as an
anti-estrogen in breast tissue, than ER-negative tumors. Though
useful, these analyses only in part predict the clinical behavior
of breast tumors. There is phenotypic diversity present in cancers
that current diagnostic tools fail to detect. As a consequence,
there is still much controversy over how to stratify patients
amongst potential treatments in order to optimize outcome (e.g.,
for breast cancer see "NIH Consensus Development Conference
Statement: Adjuvant Therapy for Breast Cancer, Nov. 1-3, 2000", J.
Nat. Cancer Inst. Monographs, 30:5-15, 2001 and Di Leo et al., Int.
J. Clin. Oncol. 7:245-253, 2002).
[0004] There clearly exists a need for improved methods and
reagents for classifying tumors. Once these methods and reagents
are available, clinical studies can be performed that will allow
the identification of classes or subclasses of patients having
different prognosis and/or responses to therapy. Such prognostic
tools will allow more rationally based choices governing the
aggressiveness of therapeutic interventions; such predictive tools
will also be useful for directing patients into appropriate
treatment protocols.
SUMMARY OF THE INVENTION
[0005] The invention encompasses the realization that particular
panels of tumor sample binding agents ("interaction partners") can
be used to provide new insights into the biology of cancer. Among
other things, the present invention provides methods and reagents
for classifying tumors and for identifying new tumor classes and
subclasses. The invention further provides methods for correlating
tumor class or subclass with therapeutic regimen or outcome, for
identifying appropriate (new or known) therapies for particular
classes or subclasses, and for predicting outcomes based on class
or subclass. The invention further provides new therapeutic agents
and methods for the treatment of cancer.
[0006] For example, the present invention provides methods for
identifying suitable panels of interaction partners (e.g., without
limitation antibodies) whose binding is correlated with any of a
variety of desirable aspects such as tumor class or subclass, tumor
source (e.g., primary tumor versus metastases), likely prognosis,
responsiveness to therapy, etc. Specifically, collections of
interaction partners are selected and their activity in binding to
a variety of different tumors, normal tissues and/or cell lines is
assessed. Data are collected for multiple interaction partners to
multiple samples and correlations with interesting or desirable
features are assessed. As described herein, the detection of
individual interaction partners or panels thereof that bind
differentially with different tumors provides new methods of use in
cancer prognosis and treatment selection. In addition, these
interaction partners provide new therapies for treating cancer.
[0007] As described in further detail below, the invention employs
methods for grouping interaction partners within a panel into
subsets by determining their binding patterns across a collection
of samples obtained from different tumor tissues, normal tissues
and/or cell lines. The invention also groups the tumor samples into
classes or subclasses based on similarities in their binding to a
panel of interaction partners. This two-dimensional grouping
approach permits the association of particular classes of tumors
with particular subsets of interaction partners that, for example,
show relatively high binding to tumors within that class.
Correlation with clinical information indicates that the tumor
classes have clinical significance in terms of prognosis or
response to chemotherapy.
BRIEF DESCRIPTION OF APPENDICES A-D
[0008] This patent application refers to material comprising tables
and data presented as appendices.
[0009] Appendix A is a table that lists the antibodies included in
the breast, lung and/or colon classification panels that are
discussed in Examples 2-6. The table includes the antibody ID,
parent gene name, NCBI LocusLink ID, UniGene ID, known aliases for
the parent gene, peptides that were used in preparing antibodies,
antibody titer and a link to any relevant IHC images of Appendix B.
Antibodies with AGI IDs that begin with S5 or S6 were obtained from
commercial sources as indicated. The third and fourth columns of
Appendix A indicate whether the antibodies of the breast cancer
classification panel were identified by staining with the Russian
breast cohort (Example 2) and/or the HH breast cohort (Example 3).
The fifth and sixth columns indicate whether the antibodies of the
lung cancer classification panel were identified by staining with
the Russian lung cohort (Example 4) and/or the HH lung cohort
(Example 5). The seventh column indicates the antibodies of the
colon cancer classification panel. These were all identified by
staining with the Russian colon cohort (Example 6).
[0010] Appendix B includes breast IHC images, colon IHC images and
lung IHC images. The IHC images of Appendix B are referenced in the
right hand column of Appendix A.
[0011] Appendix C is a table that lists exemplary antibodies whose
binding patterns have been shown to correlate with tumor prognosis
in breast cancer patients. The results are grouped into four
categories that have been clinically recognized to be of
significance: all patients, ER+ patients, ER- patients, and
ER+/lymph node metastases negative patients. Scoring methods 1-3
use the following schemes: method 1 (0=negative; 1=weak; 2=strong);
method 2 (0=negative; 1=weak or strong); and method 3 (0=negative
or weak; 1=strong).
[0012] Appendix D is a table that lists exemplary antibodies whose
binding patterns have been shown to correlate with tumor prognosis
in lung cancer patients. The results are grouped into three
categories that have been clinically recognized to be of
significance: all patients, adenocarcinoma patients, and squamous
cell carcinoma patients. Scoring methods 1-3 use the following
schemes: method 1 (0=negative; 1=weak; 2=strong); method 2
(0=negative; 1=weak or strong); and method 3 (0=negative or weak;
1=strong).
BRIEF DESCRIPTION OF THE DRAWING
[0013] FIG. 1 depicts semi-quantitative immunohistochemistry (IHC)
scoring of a 298 breast cancer patient cohort with an inventive
breast cancer classification panel. The panel was prepared as
described in Example 2--antibodies were used as interaction
partners. The patients (rows) were classified using k-means
clustering while the antibodies (columns) were organized using
hierarchical clustering. Dark gray represents strong positive
staining, black represents weak positive staining, while light gray
represents the absence of staining and medium gray represents a
lack of data. As illustrated in the Figure, nine groups of patients
were identified by their consensus pattern of staining with the
panel of antibodies.
[0014] FIG. 2 depicts semi-quantitative immunohistochemistry (IHC)
scoring of a 387 lung cancer patient cohort with an inventive lung
cancer classification panel. The panel was prepared as described in
Example 4--antibodies were used as interaction partners. The
patients (rows) were classified using k-means clustering while the
antibodies (columns) were organized using hierarchical clustering.
Dark gray represents strong positive staining, black represents
weak positive staining, while light gray represents the absence of
staining and medium gray represents a lack of data. As illustrated
in the Figure, eight groups of patients were identified by their
consensus pattern of staining with the panel of antibodies.
[0015] FIG. 3 depicts semi-quantitative immunohistochemistry (IHC)
scoring of a 359 colon cancer patient cohort with an inventive
colon cancer classification panel. The panel was prepared as
described in Example 6--antibodies were used as interaction
partners. The patients (rows) were classified using k-means
clustering while the antibodies (columns) were organized using
hierarchical clustering. Dark gray represents strong positive
staining, black represents weak positive staining, while light gray
represents the absence of staining and medium gray represents a
lack of data. As illustrated in the Figure, seven groups of
patients were identified by their consensus pattern of staining
with the panel of antibodies.
[0016] FIG. 4 shows Kaplan-Meier curves that were generated for ER+
patients after prognostic classification based on (A) staining with
a prognostic panel of antibodies from Appendix C and (B) the
Nottingham Prognostic Index (NPI). In each case the patients were
placed into one of three prognostic groups, namely "poor" (bottom
curve), "moderate" (middle curve) and "good" (top curve).
[0017] FIG. 5 shows Kaplan-Meier curves that were generated for
ER+/lymph node metastases negative patients after prognostic
classification based on (A) staining with a prognostic panel of
antibodies from Appendix C and (B) the Nottingham Prognostic Index
(NPI). In each case the patients were placed into one of three
prognostic groups, namely "poor" (bottom curve), "moderate" (middle
curve) and "good" (top curve). Note that under the NPI scheme
ER+/lymph node metastases negative patients are never categorized
as having a "poor" prognosis. For this reason, FIG. 5B only
includes curves for patients with a "moderate" or "good"
prognosis.
DEFINITIONS
[0018] Associated--When an interaction partner and a tumor marker
are physically "associated" with one another as described herein,
they are linked by direct non-covalent interactions. Desirable
non-covalent interactions include those of the type which occur
between an immunoglobulin molecule and an antigen for which the
immunoglobulin is specific, for example, ionic interactions,
hydrogen bonds, van der Waals interactions, hydrophobic
interactions, etc. The strength, or affinity of the physical
association can be expressed in terms of the dissociation constant
(K.sub.d) of the interaction, wherein a smaller K.sub.d represents
a greater affinity. The association properties of selected
interaction partners and tumor markers can be quantified using
methods well known in the art (e.g., see Davies et al., Annual Rev.
Biochem. 59:439, 1990).
[0019] Classification panel--A "classification panel" of
interaction partners is a set of interaction partners whose
collective pattern of binding or lack of binding to a tumor sample,
when taken together, is sufficient to classify the tumor sample as
a member of a particular class or subclass of tumor, or as not a
member of a particular class or subclass of tumor.
[0020] Correlation--"Correlation" refers to the degree to which one
variable can be predicted from another variable, e.g., the degree
to which a patient's therapeutic response can be predicted from the
pattern of binding between a set of interaction partners and a
tumor sample taken from that patient. A variety of statistical
methods may be used to measure correlation between two variables,
e.g., without limitation the student t-test, the Fisher exact test,
the Pearson correlation coefficient, the Spearman correlation
coefficient, the Chi squared test, etc. Results are traditionally
given as a measured correlation coefficient with a p-value that
provides a measure of the likelihood that the correlation arose by
chance. A correlation with a p-value that is less than 0.05 is
generally considered to be statistically significant. Preferred
correlations have p-values that are less than 0.01, especially less
than 0.001.
[0021] Interaction partner--An "interaction partner" is an entity
that physically associates with a tumor marker. For example and
Without limitation, an interaction partner may be an antibody or a
fragment thereof that physically associates with a tumor marker. In
general, an interaction partner is said to "associate specifically"
with a tumor marker if it associates at a detectable level with the
tumor marker and does not associate detectably with unrelated
molecular entities (e.g., other tumor markers) under similar
conditions. Specific association between a tumor marker and an
interaction partner will typically be dependent upon the presence
of a particular structural feature of the target tumor marker such
as an antigenic determinant or epitope recognized by the
interaction partner. Generally, if an interaction partner is
specific for epitope A, the presence of a molecular entity (e.g., a
protein) containing epitope A or the presence of free unlabeled A
in a reaction containing both free labeled A and the interaction
partner thereto, will reduce the amount of labeled A that binds to
the interaction partner. In general, it is to be understood that
specificity need not be absolute. For example, it is well known in
the art that antibodies frequently cross-react with other epitopes
in addition to the target epitope. Such cross-reactivity may be
acceptable depending upon the application for which the interaction
partner is to be used. Thus the degree of specificity of an
interaction partner will depend on the context in which it is being
used. In general, an interaction partner exhibits specificity for a
particular tumor marker if it favors binding with that partner
above binding with other potential partners, e.g., other tumor
markers. One of ordinary skill in the art will be able to select
interaction partners having a sufficient degree of specificity to
perform appropriately in any given application (e.g., for detection
of a target tumor marker, for therapeutic purposes, etc.). It is
also to be understood that specificity may be evaluated in the
context of additional factors such as the affinity of the
interaction partner for the target tumor marker versus the affinity
of the interaction partner for other potential partners, e.g.,
other tumor markers. If an interaction partner exhibits a high
affinity for a target tumor marker and low affinity for non-target
molecules, the interaction partner will likely be an acceptable
reagent for diagnostic purposes even if it lacks specificity. It
will be appreciated that once the specificity of an interaction
partner is established in one or more contexts, it may be employed
in other, preferably similar, contexts without necessarily
re-evaluating its specificity.
[0022] Predictive panel--A "predictive panel" of interaction
partners is a set of interaction partners whose collective pattern
of binding or lack of binding to a tumor sample, when taken
together, has sufficient correlation to classify the tumor sample
as being from a patient who is likely (or not) to respond to a
given therapeutic regimen.
[0023] Prognostic panel--A "prognostic panel" of interaction
partners is a set of interaction partners whose collective pattern
of binding or lack of binding to a tumor sample, when taken
together, has sufficient correlation to classify the tumor sample
as being from a patient who is likely to have a given outcome.
Generally, "outcome" may include, but is not limited to, the
average life expectancy of the patient, the likelihood that the
patient will survive for a given amount of time (e.g., 6 months, 1
year, 5 years, etc.), the likelihood of recurrence, the likelihood
that the patient will be disease-free for a specified prolonged
period of time, or the likelihood that the patient will be cured of
the disease.
[0024] Response--The "response" of a tumor or a cancer to therapy
may represent any detectable change, for example at the molecular,
cellular, organellar, or organismal level. For instance, tumor
size, patient life expectancy, recurrence, or the length of time
the patient survives, etc., are all responses. Responses can be
measured in any of a variety of ways, including for example
non-invasive measuring of tumor size (e.g., CT scan, image-enhanced
visualization, etc.), invasive measuring of tumor size (e.g.,
residual tumor resection, etc.), surrogate marker measurement
(e.g., serum PSA, etc.), clinical course variance (e.g.,
measurement of patient quality of life, time to relapse, survival
time, etc.).
[0025] Small molecule--A "small molecule" is a non-polymeric
molecule. A small molecule can be synthesized in a laboratory
(e.g., by combinatorial synthesis) or found in nature (e.g., a
natural product). A small molecule is typically characterized in
that it contains several carbon-carbon bonds and has a molecular
weight of less than about 1500 Da, although this characterization
is not intended to be limiting for the purposes of the present
invention.
[0026] Tumor markers--"Tumor markers" are molecular entities that
are detectable in tumor samples. Generally, tumor markers will be
proteins that are present within the tumor sample, e.g., within the
cytoplasm or membranes of tumor cells and/or secreted from such
cells. According to the present invention, sets of tumor markers
that correlate with tumor class or subclass are identified. Thus,
subsequent tumor samples may be classified or subclassified based
on the presence of these sets of tumor markers.
[0027] Tumor sample--As used herein the term "tumor sample" is
taken broadly to include cell or tissue samples removed from a
tumor, cells (or their progeny) derived from a tumor that may be
located elsewhere in the body (e.g., cells in the bloodstream or at
a site of metastasis), or any material derived by processing such a
sample. Derived tumor samples may include, for example, nucleic
acids or proteins extracted from the sample.
DETAILED DESCRIPTION OF CERTAIN PREFERRED EMBODIMENTS OF THE
INVENTION
[0028] As noted above, the present invention provides techniques
and reagents for the classification and subclassification, of
tumors. Such classification (or subclassification) has many
beneficial applications. For example, a particular tumor class or
subclass may correlate with prognosis and/or susceptibility to a
particular therapeutic regimen. As such, the classification or
subclassification may be used as the basis for a prognostic or
predictive kit and may also be used as the basis for identifying
previously unappreciated therapies. Therapies that are effective
against only a particular class or subclass of tumor may have been
lost in studies whose data were not stratified by subclass; the
present invention allows such data to be re-stratified, and allows
additional studies to be performed, so that class- or
subclass-specific therapies may be identified and/or implemented.
Alternatively or additionally, the present invention allows
identification and/or implementation of therapies that are targeted
to genes identified as class- or subclass-specific.
[0029] Classification and Subclassification of Tumors
[0030] In general, according to the present invention, tumors are
classified or subclassified on the basis of tumor markers whose
presence is correlated with a particular class or subclass. In
preferred embodiments, the tumor markers are detected via their
physical association with an interaction partner. Included in the
present invention are kits comprising sets of interaction partners
that together can be used to identify or classify a particular
tumor sample; such sets are generally referred to as
"classification panels".
[0031] The present invention provides systems of identifying
classification panels. In general, tumor samples are contacted with
individual interaction partners, and binding between the
interaction partners and their cognate tumor markers is detected.
For example, panels of interaction partners that identify a
particular class or subclass of tumor within tumor samples of a
selected tissue of origin may be defined by contacting individual
interaction partners with a variety of different tumor samples
(e.g., from different patients) all of the same tissue of origin.
Individual interaction partners may be selected for inclusion in
the ultimate classification panel based on their binding to only a
subset of the tumor samples (e.g., see Examples 1-4). Those of
ordinary skill in the art, however, will appreciate that all that
is required for a collection of interaction partners to operate
effectively as a classification panel is that the combined binding
characteristics of member interaction partners together are
sufficient to classify a particular tumor sample.
[0032] The inventive process of identifying useful panels of
interaction partners as described herein may itself result in the
identification of new tumor classes or subclasses. That is, through
the process of analyzing interaction partner binding patterns,
investigators will often discover new tumor classes or subclasses
to which sets of interaction partners bind. Thus, the processes (a)
of defining classification panels of interaction partners for given
tumor classes or subclasses; and (b) identifying new tumor classes
or subclasses may well be experimentally interrelated. In general,
the greater the number of tumor samples tested, the greater the
likelihood that new classes or subclasses will be defined.
[0033] Often, when identifying sets of interaction partners that
can act as a classification (or subclassification) panel, it will
be desirable to obtain the largest set of tumor samples possible,
and also to collect the largest amount of information possible
about the individual samples. For example, the origin of the tumor,
the gender of the patient, the age of the patient, the staging of
the tumor (e.g., according to the TNM system), any microscopic or
submicroscopic characteristics of the tumor that may have been
determined, may be recorded. Those of ordinary skill in the art
will appreciate that the more information that is known about a
tumor sample, the more aspects of that sample are available for
correlation with interaction partner binding.
[0034] The systems of the present invention have particular utility
in classifying or subclassifying tumor samples that are not
otherwise distinguishable from one another. Thus, in some
embodiments, it will be desirable to analyze the largest collection
of tumor samples that are most similar to one another.
[0035] When obtaining tumor samples for testing according to the
present invention, it is generally preferred that the samples
represent or reflect characteristics of a population of patients or
samples. It may also be useful to handle and process the samples
under conditions and according to techniques common to clinical
laboratories. Although the present invention is not intended to be
limited to the strategies used for processing tumor samples, we
note that, in the field of pathology, it is often common to fix
samples in buffered formalin, and then to dehydrate them by
immersion in increasing concentrations of ethanol followed by
xylene. Samples are then embedded into paraffin, which is then
molded into a "paraffin block" that is a standard intermediate in
histologic processing of tissue samples. The present inventors have
found that many useful interaction partners display comparable
binding regardless of the method of preparation of tumor samples;
those of ordinary skill in the art can readily adjust observations
to account for differences in preparation procedure.
[0036] In preferred embodiments of the invention, large numbers of
tissue samples are analyzed simultaneously. In some embodiments, a
tissue array is prepared. Tissue arrays may be constructed
according to a variety of techniques. According to one procedure, a
commercially-available mechanical device (e.g., the manual tissue
arrayer MTA1 from Beecher Instruments of Sun Prairie, Wis.) is used
to remove an 0.6-micron-diameter, full thickness "core" from a
paraffin block (the donor block) prepared from each patient, and to
insert the core into a separate paraffin block (the recipient
block) in a designated location on a grid. In preferred
embodiments, cores from as many as about 400 patients can be
inserted into a single recipient block; preferably, core-to-core
spacing is approximately 1 mm. The resulting tissue array may be
processed into thin sections for staining with interaction partners
according to standard methods applicable to paraffin embedded
material. Depending upon the thickness of the donor blocks, as well
as the dimensions of the clinical material, a single tissue array
can yield about 50-150 slides containing >75% relevant tumor
material for assessment with interaction partners. Construction of
two or more parallel tissue arrays of cores from the same cohort of
patient samples can provide relevant tumor material from the same
set of patients in duplicate or more. Of course, in some cases,
additional samples will be present in one array and not
another.
[0037] The present inventors have found that it is often desirable
to evaluate some aspects of the binding characteristics of
potential interaction partners before or while assessing the
desirability of including them in an interaction panel. For
example, the inventors have found that it is often desirable to
perform a titration study in which different concentrations of the
interaction partner are contacted with a diverse set of tissue
samples derived from a variety of different tissues (e.g., normal
and/or tumor) in order to identify a concentration or titer at
which differential binding is observed. This titer is referred to
herein as a "discriminating titer". Such differential staining may
be observed between different tissue samples and/or between
different cell types within a given tissue sample.
[0038] In general, any tissue sample may be used for this purpose
(e.g., samples obtained from the epididymis, esophagus, gall
bladder, kidneys, liver, lungs, lymph nodes, muscles, ovaries,
pancreas, parathyroid glands, placenta, prostate, saliva, skin,
spleen, stomach, testis, thymus, thyroid, tonsils, uterus, etc.).
For such titration studies, greater diversity among samples is
often preferred. Without intending to limit the present invention,
the inventors observe that useful titers for particular interaction
partners can typically be defined in a study of approximately 40-70
different tissue samples from about 20-40 different tissues.
[0039] Binding studies (for titration, for assessment of inclusion
in a panel, or during use of a panel) may be performed in any
format that allows specific interaction to be detected. Where large
numbers of samples are to be handled, it may be desirable to
utilize arrayed and/or automated formats. Particularly preferred
formats include tissue arrays as discussed above. The staining of
large numbers of samples derived from a variety of tumors in a
tissue array format allows excellent comparative assessment of
differential staining between or among samples under identical
conditions. According to the present invention, staining patterns
that identify at least about 10% of samples as binding with a
particular interaction partner, or at least about 20, 30, 40, 50%
or more of samples, are likely to represent "real" differential
staining patterns (i.e., real variations in binding with
interaction partner and not experimental variations, for example,
due to sample processing or day to day variation in staining
techniques).
[0040] Any available technique may be used to detect binding
between an interaction partner and a tumor sample. One powerful and
commonly used technique is to have a detectable label associated
(directly or indirectly) with the interaction partner. For example,
commonly-used labels that often are associated with antibodies used
in binding studies include fluorochromes, enzymes, gold, iodine,
etc. Tissue staining by bound interaction partners is then
assessed, preferably by a trained pathologist or cytotechnologist.
For example, a scoring system may be utilized to designate whether
the interaction partner does or does not bind to (e.g., stain) the
sample, whether it stains the sample strongly or weakly and/or
whether useful information could not be obtained (e.g., because the
sample was lost, there was no tumor in the sample or the result was
otherwise ambiguous). Those of ordinary skill in the art will
recognize that the precise characteristics of the scoring system
are not critical to the invention. For example, staining may be
assessed qualitatively or quantitatively; more or less subtle
gradations of staining may be defined; etc.
[0041] Whatever the format, and whatever the detection strategy,
identification of a discriminating titer can simplify binding
studies to assess the desirability of including a given interaction
partner in a panel. In such studies, the interaction partner is
contacted with a plurality of different tumor samples that
preferably have at least one common trait (e.g., tissue of origin),
and often have multiple common traits (e.g., tissue of origin,
stage, microscopic characteristics, etc.). In some cases, it will
be desirable to select a group of samples with at least one common
trait and at least one different trait, so that a panel of
interaction partners is defined that distinguishes the different
trait. In other cases, it will be desirable to select a group of
samples with no detectable different traits, so that a panel of
interaction partners is defined that distinguishes among previously
indistinguishable samples. Those of ordinary skill in the art will
understand, however, that the present invention often will allow
both of these goals to be accomplished even in studies of sample
collections with varying degrees of similarity and difference.
[0042] According to the present invention, interaction partners
that bind to tumor samples may be characterized by their ability to
discriminate among tumor samples. Any of a variety of techniques
may be used to identify discriminating interaction partners. To
give but one example, the present inventors have found it useful to
define a "consensus panel" of tissue samples for tumors of a
particular tissue of origin (see Examples 2-6). Those of ordinary
skill in the art will again appreciate that the precise parameters
used to designate a particular sample as interpretable and
reproducible are not critical to the invention. Interaction
partners may then be classified based on their ability to
discriminate among tissue samples in the consensus panel (see
Examples 2-6).
[0043] Assessing Prognosis or Therapeutic Regimen
[0044] The present invention further provides systems for
identifying panels of interaction partners whose binding correlates
with factors beyond tumor class or subclass, such as likelihood of
a particular favorable or unfavorable outcome, susceptibility (or
lack thereof) to a particular therapeutic regimen, etc.
[0045] As mentioned in the background, current approaches to
assigning prognostic probabilities and/or selecting appropriate
therapeutic regimens for particular tumors generally utilize the
tumor-node-metastasis (TNM) system. This system uses the size of
the tumor, the presence or absence of tumor in regional lymph nodes
and the presence or absence of distant metastases, to assign a
stage to the tumor. The assigned stage is used as a basis for
selection of appropriate therapy and for prognostic purposes.
[0046] The present invention provides new methods and systems for
evaluating tumor prognosis and/or recommended therapeutic
approaches. In particular, the present invention provides systems
for identifying panels of interaction partners whose binding
correlates with tumor prognosis or therapeutic outcome.
[0047] For example, interaction partners whose binding correlates
with prognosis can be identified by evaluating their binding to a
collection of tumor samples for which prognosis is known or
knowable. That is, the strategies of the invention may be employed
either to identify collections of interaction partners whose
binding correlates with a known outcome, or may be employed to
identify a differential staining pattern that is then correlated
with outcome (which outcome may either be known in advance or
determined over time).
[0048] In general, it is preferred that inventive binding analyses
be performed on human tumor samples. However, it is not necessary
that the human tumors grow in a human host. Particularly for
studies in which long-term outcome data are of interest (especially
prognostic or predictive studies), it can be particularly useful to
analyze samples grown in vitro (e.g., cell lines) or, more
preferably, in a non-human host (e.g., a rodent, a dog, a sheep, a
pig, or other animal). For instance, Example 9 provides a
description of an assay in which inventive techniques employing
human tumor cells growing in a non-human host are employed to
define and/or utilize a panel of interaction partners whose binding
to tumor samples correlates with prognosis and/or responsiveness to
therapy.
[0049] It will often be desirable, when identifying interaction
partners whose binding correlates with prognosis, to collect
information about treatment regimens that may have been applied to
the tumor whose sample is being assessed, in order to control for
effects attributable to tumor therapy. Prognostic panel binding may
correlate with outcome independent of treatment (Hayes et al., J
Mamm. Gland Bio. Neo. 6:375, 2001). Many prognostic markers,
however, have both prognostic and predictive character (e.g.,
Her2/Neu status). Many of the individual interaction partners that
comprise a prognostic panel may likewise have predictive capability
and/or be members of a predictive panel.
[0050] Those of ordinary skill in the art will appreciate that
prognostic panels (or individual interaction partners) have greater
clinical utility if their binding/lack thereof correlates with
positive/negative outcomes that are well separated
statistically.
[0051] The inventive strategies may also be applied to the
identification of predictive panels of interaction partners (i.e.,
panels whose binding correlates with susceptibility to a particular
therapy). As noted above, some prognostic panels may also have
predictive capabilities.
[0052] Interaction partners to be included in predictive panels are
identified in binding studies performed on tumor samples that do or
do not respond to a particular therapy. As with the prognostic
panels, predictive panels may be assembled based on tests of tumor
samples whose responsiveness is already known, or on samples whose
responsiveness is not known in advance. As with the prognostic
studies discussed above, the source of the tumor samples is not
essential and can include, for example, tumor cell lines whose
responsiveness to particular chemical agents has been determined,
tumor samples from animal models in which tumors have been
artificially introduced and therapeutic responsiveness has been
determined and/or samples from naturally-occurring (human or other
animal) tumors for which outcome data (e.g., time of survival,
responsiveness to therapy, etc.) are available. Panels of
interaction partners whose binding to tumor samples correlates with
any prognostic or therapeutic trend can be defined and utilized as
described herein.
[0053] Once correlations between interaction partner binding and
tumor behavior have been established, the defined prognostic or
predictive panels can be used to evaluate and classify tumor
samples from patients and can be relied upon, for example to guide
selection of an effective therapeutic regimen. As with the tumor
classification studies described above, the process of identifying
interaction partner panels whose binding correlates with outcome
may itself identify particular outcomes not previously appreciated
as distinct.
[0054] Those of ordinary skill in the art will appreciate that it
is likely that, in at least some instances, tumor class or subclass
identity will itself correlate with prognosis or responsiveness. In
such circumstances, it is possible that the same set of interaction
partners can act as both a classification panel and a prognosis or
predictive panel.
[0055] Tumor Elements Bound By Interaction Partners
[0056] The inventive strategies for identifying and utilizing
interaction partner panels in classifying or analyzing tumor
samples do not rely on any assumptions about the identity or
characteristics of the tumor components bound by the interaction
partners. So long as interaction partner binding within the
relevant panel correlates with some feature of interest, the
inventive teachings apply. In many if not most, cases, however, it
is expected that binding will be with a protein expressed by tumor
cells.
[0057] In some preferred embodiments of the invention, interaction
partners bind to tumor markers that (a) are differentially
expressed in tumor cells; (b) are members of protein families whose
activities contribute to relevant biological events (e.g., gene
families that have been implicated in cancer such as oncogenes,
tumor suppressor genes, and genes that regulate apoptosis; gene
families that have been implicated in drug resistance; etc.); (c)
are present on or in the plasma membrane of the tumor cells; and/or
(d) are the products of degradation of tumor components, which
degradation products might be detectable in patient serum.
[0058] In fact, according to the present invention, interaction
partners for analysis and use in inventive panels may sometimes be
identified by first identifying a tumor-associated protein of
interest, and then finding a potential interaction partner that
binds with the protein. Binding by this potential interaction
partner to tumor samples may then be assessed and utilized as
described herein.
[0059] For example, as described in the Examples, the present
inventors have successfully assembled classification panels
comprised of antibodies that bind to tumor protein antigens.
Candidate antigens were identified both through literature reviews
of proteins that play a biological role in tumor initiation or
progression, or that are known to be differentially expressed in
tumors, and through gene expression studies that identified
additional differentially expressed proteins.
[0060] Work by the present inventors, as well as by others, has
already demonstrated that studies of gene expression patterns in
large tumor cohorts can identify novel tumor classes (see, for
example, Perou et al., Nature 406:747, 2000; Sorlie et al., Proc
Natl Acad. Sci. USA 98:10869, 2001; van't Veer et al., Nature
415:530, 2002; West et al., Proc Natl. Acad. Sci. USA 98:11462,
2001; Hedenfalk et al., N. Engl. J. Med. 344:539, 2001; Gruvberger
et al., Cancer Res. 61:5979, 2001; MacDonald et al., Nature Genet.
29:143, 2001; Pomeroy et al., Nature 415:436, 2002; Jazaeri et al.,
J Natl Cancer Inst 94:990, 2002; Welsh et al., Proc. Natl. Acad.
Sci. USA 98:1176, 2001; Wang et al., Gene 229:101, 1999; Beer et
al., Nature Med. 8:816, 2002; Garber et al., Proc Natl Acad Sci USA
98:13784, 2001; Bhattacharjee et al., Proc Natl Acad Sci USA
98:13790, 2001; Zou et al., Oncogene 21:4855, 2002; Lin et al.,
Oncogene 21:4120, 2002; Alon et al., Proc Natl Acad Sci USA
96:6745, 1999; Takahashi et al., Proc Natl Acad Sci USA 98:9754,
2001; Singh et al., Cancer Cell 1:203, 2002; LaTulippe et al.,
Cancer Res. 62:4499, 2002; Welsh et al., Cancer Res. 61:5974, 2001;
Dhanasekaran et al., Nature 412:822, 2001; Hippo et al., Cancer
Res. 62:233, 2002; Yeoh et al., Cancer Cell 1:133, 2002; Hofmann et
al., Lancet 359:481, 2002; Ferrando et al., Cancer Cell 1:75, 2002;
Shipp et al., Nature Med 8:68, 2002; Rosenwald et al., N. Engl. J.
Med. 346:1937, 2002; and Alizadeh et al., Nature 403:503, 2000,
each of which is incorporated herein by reference).
[0061] The gene sets described in these publications are promising
candidates for genes that are likely to encode tumor markers whose
interaction partners are useful in tumor classification and
subclassification according to the present invention. Of particular
interest are gene sets differentially expressed in solid
tumors.
[0062] Furthermore, in general, given that differentially expressed
genes are likely to be responsible for the different phenotypic
characteristics of tumors, the present invention recognizes that
such genes will often encode tumor markers for which a useful
interaction partner, that discriminates among tumor classes or
subclasses, can likely be prepared. A differentially expressed gene
is a gene whose transcript abundance varies between different
samples, e.g., between different tumor samples, between normal
versus tumor samples, etc. In general, the amount by which the
expression varies and the number of samples in which the expression
varies by that amount will depend upon the number of samples and
the particular characteristics of the samples. One skilled in the
art will be able to determine, based on knowledge of the samples,
what constitutes a significant degree of differential expression.
Such genes can be identified by any of a variety of techniques
including, for instance, in situ hybridization, Northern blot,
nucleic acid amplification techniques (e.g., PCR, quantitative PCR,
the ligase chain reaction, etc.), and, most commonly, microarray
analysis.
[0063] Furthermore, those of ordinary skill in the art will readily
appreciate, reading the present disclosure, that the inventive
processes described herein of identifying and/or using sets of
interaction partners whose binding (or lack thereof) correlates
with an interesting tumor feature (e.g., tumor type or subtype,
patient outcome, responsiveness of tumor or patient to therapy,
etc.) inherently identifies both interaction partners of interest
and the tumor markers to which they bind. Thus, one important
aspect of the present invention is the identification of tumor
markers whose ability (or lack thereof) to associate with an
interaction partner correlates with a tumor characteristic of
interest. Such tumor markers are useful as targets for
identification of new therapeutic reagents, as well as of
additional interaction partners useful in the practice of the
present invention. Thus, it is to be understood that discussions of
interaction partners presented herein are typically not limited to
a particular interaction partner compound or entity, but may be
generalized to include any compound or entity that binds to the
relevant tumor marker(s) with requisite specificity and
affinity.
[0064] Preparation of Interaction Partners
[0065] In general, interaction partners are entities that
physically associate with selected tumor markers. Thus, any entity
that binds detectably to a tumor marker may be utilized as an
interaction partner in accordance with the present invention, so
long as it binds with an appropriate combination of affinity and
specificity.
[0066] Particularly preferred interaction partners are antibodies,
or fragments (e.g., F(ab) fragments, F(ab').sub.2 fragments, Fv
fragments, or sFv fragments, etc.; see, for example, Inbar et al.,
Proc. Nat. Acad. Sci. USA 69:2659, 1972; Hochman et al., Biochem.
15:2706, 1976; and Ehrlich et al., Biochem. 19:4091, 1980; Huston
et al., Proc. Nat. Acad. Sci. USA 85:5879, 1998; U.S. Pat. Nos.
5,091,513 and 5,132,405 to Huston et al.; and U.S. Pat. No.
4,946,778 to Ladner et al., each of which is incorporated herein by
reference). In certain embodiments, interaction partners may be
selected from libraries of mutant antibodies (or fragments
thereof). For example, collections of antibodies that each include
different point mutations may be screened for their association
with a tumor marker of interest. Yet further, chimeric antibodies
may be used as interaction partners, e.g., "humanized" or
"veneered" antibodies as described in greater detail below.
[0067] It is to be understood that the present invention is not
limited to using antibodies or antibody fragments as interaction
partners of inventive tumor markers. In particular, the present
invention also encompasses the use of synthetic interaction
partners that mimic the functions of antibodies. Several approaches
to designing and/or identifying antibody mimics have been proposed
and demonstrated (e.g., see the reviews by Hsieh-Wilson et al.,
Acc. Chem. Res. 29:164, 2000 and Peczuh and Hamilton, Chem. Rev.
100:2479, 2000). For example, small molecules that bind protein
surfaces in a fashion similar to that of natural proteins have been
identified by screening synthetic libraries of small molecules or
natural product isolates (e.g., see Gallop et al., J. Med. Chem.
37:1233, 1994; Gordon et al., J. Med. Chem. 37:1385, 1994; DeWitt
et al., Proc. Natl. Acad. Sci. U.S.A. 90:6909, 1993; Bunin et al.,
Proc. Natl. Acad. Sci. U.S.A. 91:4708, 1994; Virgilio and Ellman,
J. Am. Chem. Soc. 116:11580, 1994; Wang et al., J. Med. Chem.
38:2995, 1995; and Kick and Ellman, J. Med. Chem. 38:1427, 1995).
Similarly, combinatorial approaches have been successfully applied
to screen libraries of peptides and polypeptides for their ability
to bind a range of proteins (e.g., see Cull et al., Proc. Natl.
Acad. Sci. U.S.A. 89:1865, 1992; Mattheakis et al., Proc. Natl.
Acad. Sci. U.S.A. 91:9022, 1994; Scott and Smith, Science 249:386,
1990; Devlin et al., Science 249:404, 1990; Corey et al., Gene
128:129, 1993; Bray et al., Tetrahedron Lett. 31:5811, 1990; Fodor
et al., Science 251:767, 1991; Houghten et al., Nature 354:84,
1991; Lam et al., Nature 354:82, 1991; Blake and Litzi-Davis,
Bioconjugate Chem. 3:510, 1992; Needels et al., Proc. Natl. Acad.
Sci. U.S.A. 90:10700, 1993; and Ohlmeyer et al., Proc. Natl. Acad.
Sci. U.S.A. 90:10922, 1993). Similar approaches have also been used
to study carbohydrate-protein interactions (e.g., see Oldenburg et
al., Proc. Natl. Acad. Sci. U.S.A. 89:5393, 1992) and
polynucleotide-protein interactions (e.g., see Ellington and
Szostak, Nature 346:818, 1990 and Tuerk and Gold, Science 249:505,
1990). These approaches have also been extended to study
interactions between proteins and unnatural biopolymers such as
oligocarbamates, oligoureas, oligosulfones, etc. (e.g., see
Zuckermann et al., J. Am. Chem. Soc. 114:10646, 1992; Simon et al.,
Proc. Natl. Acad. Sci. U.S.A. 89:9367, 1992; Zuckermann et al., J.
Med. Chem. 37:2678, 1994; Burgess et al., Angew. Chem., Int. Ed.
Engl. 34:907, 1995; and Cho et al., Science 261:1303, 1993). Yet
further, alternative protein scaffolds that are loosely based
around the basic fold of antibody molecules have been suggested and
may be used in the preparation of inventive interaction partners
(e.g., see Ku and Schultz Proc. Natl. Acad. Sci. U.S.A. 92:6552,
1995). Antibody mimics comprising a scaffold of a small molecule
such as 3-aminomethylbenzoic acid and a substituent consisting of a
single peptide loop have also been constructed. The peptide loop
performs the binding function in these mimics (e.g., see Smythe et
al., J. Am. Chem. Soc. 116:2725, 1994). A synthetic antibody mimic
comprising multiple peptide loops built around a calixarene unit
has also been described (e.g., see U.S. Pat. No. 5,770,380 to
Hamilton et al.).
[0068] Detecting Association of Interaction Partners and Tumor
Markers
[0069] Any available strategy or system may be utilized to detect
association between an interaction partner and its cognate tumor
marker. In certain embodiments, association can be detected by
adding a detectable label to the interaction partner. In other
embodiments, association can be detected by using a labeled
secondary interaction partner that associates specifically with the
primary interaction partner, e.g., as is well known in the art of
antigen/antibody detection. The detectable label may be directly
detectable or indirectly detectable, e.g., through combined action
with one or more additional members of a signal producing system.
Examples of directly detectable labels include radioactive,
paramagnetic, fluorescent, light scattering, absorptive and
colorimetric labels. Examples of indirectly detectable include
chemiluminescent labels, e.g., enzymes that are capable of
converting a substrate to a chromogenic product such as alkaline
phosphatase, horseradish peroxidase and the like.
[0070] Once a labeled interaction partner has bound a tumor marker,
the complex may be visualized or detected in a variety of ways,
with the particular manner of detection being chosen based on the
particular detectable label, where representative detection means
include, e.g., scintillation counting, autoradiography, measurement
of paramagnetism, fluorescence measurement, light absorption
measurement, measurement of light scattering and the like.
[0071] In general, association between an interaction partner and
its cognate tumor marker may be assayed by contacting the
interaction partner with a tumor sample that includes the marker.
Depending upon the nature of the sample, appropriate methods
include, but are not limited to, immunohistochemistry (IHC),
radioimmunoassay, ELISA, immunoblotting and fluorescence activates
cell sorting (FACS). In the case where the polypeptide is to be
detected in a tissue sample, e.g., a biopsy sample, IHC is a
particularly appropriate detection method. Techniques for obtaining
tissue and cell samples and performing IHC and FACS are well known
in the art.
[0072] The inventive strategies for classifying and/or
subclassifying tumor samples may be applied to samples of any type
and of any tissue of origin. In certain preferred embodiments of
the invention, the strategies are applied to solid tumors.
Historically, researchers have encountered difficulty in defining
solid tumor subtypes, given the challenges associated with defining
their molecular characteristics. As demonstrated in the Examples,
the present invention is particularly beneficial in this area.
Particularly preferred solid tumors include, for example, breast,
lung, colon, and ovarian tumors. The invention also encompasses the
recognition that tumor markers that are secreted from the cells in
which they are produced may be present in serum, enabling their
detection through a blood test rather than requiring a biopsy
specimen. An interaction partner that binds to such tumor markers
represents a particularly preferred embodiment of the
invention.
[0073] In general, the results of such an assay can be presented in
any of a variety of formats. The results can be presented in a
qualitative fashion. For example, the test report may indicate only
whether or not a particular tumor marker was detected, perhaps also
with an indication of the limits of detection. Additionally the
test report may indicate the subcellular location of binding, e.g.,
nuclear versus cytoplasmic and/or the relative levels of binding in
these different subcellular locations. The results may be presented
in a semi-quantitative fashion. For example, various ranges may be
defined and the ranges may be assigned a score (e.g., 0 to 5) that
provides a certain degree of quantitative information. Such a score
may reflect various factors, e.g., the number of cells in which the
tumor marker is detected, the intensity of the signal (which may
indicate the level of expression of the tumor marker), etc. The
results may be presented in a quantitative fashion, e.g., as a
percentage of cells in which the tumor marker is detected, as a
concentration, etc. As will be appreciated by one of ordinary skill
in the art, the type of output provided by a test will vary
depending upon the technical limitations of the test and the
biological significance associated with detection of the tumor
marker. For example, in the case of certain tumor markers a purely
qualitative output (e.g., whether or not the tumor marker is
detected at a certain detection level) provides significant
information. In other cases a more quantitative output (e.g., a
ratio of the level of expression of the tumor marker in two
samples) is necessary.
[0074] Identification of Novel Therapies
[0075] Predictive panels of interaction agents are useful according
to the present invention not only to classify tumor samples
obtained from cancer sufferers with respect to their likely
responsiveness to known therapies, but also to identify potential
new therapies or therapeutic agents that could be useful in the
treatment of cancer.
[0076] For example, as noted above, the process of identifying or
using inventive panels according to the present invention
simultaneously identifies and/or characterizes tumor markers in or
on the tumor cells that correlate with one or more selected tumor
characteristics (e.g., tumor type or subtype, patient prognosis,
and/or responsiveness of tumor or patient to therapy). Such tumor
markers are attractive candidates for identification of new
therapeutic agents (e.g., via screens to detect compounds or
entities that bind to the tumor markers, preferably with at least a
specified affinity and/or specificity, and/or via screens to detect
compounds or entities that modulate (i.e., increase or decrease)
expression, localization, modification, or activity of the tumor
markers. In many instances, interaction partners themselves may
prove to be useful therapeutics.
[0077] Thus the present invention provides interaction partners
that are themselves useful therapeutic agents. For example, binding
by an interaction partner, or a collection of interaction partners,
to a cancer cell, might inhibit growth of that cell. Alternatively
or additionally, interaction partners defined or prepared according
to the present invention could be used to deliver a therapeutic
agent to a cancer cell. In particular, interaction partners may be
coupled to one or more therapeutic agents. Suitable agents in this
regard include radionuclides and drugs. Preferred radionuclides
include .sup.90Y, .sup.123I, .sup.125I, .sup.131I, .sup.186Re,
.sup.188Re, .sup.211At and .sup.212Bi. Preferred drugs include
chlorambucil, ifosphamide, meclorethamine, cyclophosphamide,
carboplatin, cisplatin, procarbazine, decarbazine, carmustine,
cytarabine, hydroxyurea, mercaptopurine, methotrexate, thioguanine,
5-fluorouracil, actinomycin D, bleomycin, daunorubicin,
doxorubicin, etoposide, vinblastine, vincristine, L-asparginase,
adrenocorticosteroids, canciclovir triphosphate, adenine
arabinonucleoside triphosphate,
5-aziridinyl-4-hydroxylamino-2-nitrobenza- mide, acrolein,
phosphoramide mustard, 6-methylpurine, etoposide, methotrexate,
benzoic acid mustard, cyanide and nitrogen mustard.
[0078] According to such embodiments, the therapeutic agent may be
coupled with an interaction partner by direct or indirect covalent
or non-covalent interactions. A direct interaction between a
therapeutic agent and an interaction partner is possible when each
possesses a substituent capable of reacting with the other. For
example, a nucleophilic group, such as an amino or sulfhydryl
group, on one may be capable of reacting with a carbonyl-containing
group, such as an anhydride or an acid halide, or with an alkyl
group containing a good leaving group (e.g., a halide) on the
other. Indirect interactions might involve a linker group that is
itself associated with both the therapeutic agent and the
interaction partner. A linker group can function as a spacer to
distance an interaction partner from an agent in order to avoid
interference with association capabilities. A linker group can also
serve to increase the chemical reactivity of a substituent on an
agent or an interaction partner and thus increase the coupling
efficiency. An increase in chemical reactivity may also facilitate
the use of agents, or functional groups on agents, which otherwise
would not be possible.
[0079] It will be evident to those skilled in the art that a
variety of bifunctional or polyfunctional reagents, both homo- and
hetero-functional (such as those described in the catalog of the
Pierce Chemical Co., Rockford, Ill.), may be employed as the linker
group. Coupling may be effected, for example, through amino groups,
carboxyl groups, sulfydryl groups or oxidized carbohydrate
residues. There are numerous references describing such
methodology, e.g., U.S. Pat. No. 4,671,958, to Rodwell et al. It
will further be appreciated that a therapeutic agent and an
interaction partner may be coupled via non-covalent interactions,
e.g., ligand/receptor type interactions. Any ligand/receptor pair
with a sufficient stability and specificity to operate in the
context of the invention may be employed to couple a therapeutic
agent and an interaction partner. To give but an example, a
therapeutic agent may be covalently linked with biotin and an
interaction partner with avidin. The strong non-covalent binding of
biotin to avidin would then allow for coupling of the therapeutic
agent and the interaction partner. Typical ligand/receptor pairs
include protein/co-factor and enzyme/substrate pairs. Besides the
commonly used biotin/avidin pair, these include without limitation,
biotin/streptavidin, digoxigenin/anti-digoxigenin,
FK506/FK506-binding protein (FKBP), rapamycin/FKBP,
cyclophilin/cyclosporin and glutathione/glutathione transferase
pairs. Other suitable ligand/receptor pairs would be recognized by
those skilled in the art, e.g., monoclonal antibodies paired with a
epitope tag such as, without limitation, glutathione-S-transferase
(GST), c-myc, FLAG.RTM. and maltose binding protein (MBP) and
further those described in Kessler pp. 105-152 of Advances in
Mutagenesis" Ed. by Kessler, Springer-Verlag, 1990; "Affinity
Chromatography: Methods and Protocols (Methods in Molecular
Biology)" Ed. by Pascal Baillon, Humana Press, 2000; and
"Immobilized Affinity Ligand Techniques" by Hermanson et al.,
Academic Press, 1992.
[0080] Where a therapeutic agent is more potent when free from the
interaction partner, it may be desirable to use a linker group
which is cleavable during or upon internalization into a cell. A
number of different cleavable linker groups have been described.
The mechanisms for the intracellular release of an agent from these
linker groups include cleavage by reduction of a disulfide bond
(e.g., U.S. Pat. No. 4,489,710 to Spitler), by irradiation of a
photolabile bond (e.g., U.S. Pat. No. 4,625,014 to Senter et al.),
by hydrolysis of derivatized amino acid side chains (e.g., U.S.
Pat. No. 4,638,045 to Kohn et al.), by serum complement-mediated
hydrolysis (e.g., U.S. Pat. No. 4,671,958 to Rodwell et al.) and by
acid-catalyzed hydrolysis (e.g., U.S. Pat. No. 4,569,789 to
Blattler et al.).
[0081] In certain embodiments, it may be desirable to couple more
than one therapeutic agent to an interaction partner. In one
embodiment, multiple molecules of an agent are coupled to one
interaction partner molecule. In another embodiment, more than one
type of therapeutic agent may be coupled to one interaction partner
molecule. Regardless of the particular embodiment, preparations
with more than one agent may be prepared in a variety of ways. For
example, more than one agent may be coupled directly to an
interaction partner molecule, or linkers that provide multiple
sites for attachment can be used.
[0082] Alternatively, a carrier can be used. A carrier may bear the
agents in a variety of ways, including covalent bonding either
directly or via a linker group. Suitable carriers include proteins
such as albumins (e.g., U.S. Pat. No. 4,507,234 to Kato et al.),
peptides, and polysaccharides such as aminodextran (e.g., U.S. Pat.
No. 4,699,784 to Shih et al.). A carrier may also bear an agent by
non-covalent bonding or by encapsulation, such as within a liposome
vesicle (e.g., U.S. Pat. Nos. 4,429,008 to Martin et al. and
4,873,088 to Mayhew et al.). Carriers specific for radionuclide
agents include radiohalogenated small molecules and chelating
compounds. For example, U.S. Pat. No. 4,735,792 to Srivastava
discloses representative radiohalogenated small molecules and their
synthesis. A radionuclide chelate may be formed from chelating
compounds that include those containing nitrogen and sulfur atoms
as the donor atoms for binding the metal, or metal oxide,
radionuclide. For example, U.S. Pat. No. 4,673,562 to Davison et
al. discloses representative chelating compounds and their
synthesis.
[0083] When interaction partners are themselves therapeutics, it
will be understood that, in many cases, any interaction partner
that binds with the same tumor marker may be so used.
[0084] In one preferred embodiment of the invention, the
therapeutic agents (whether interaction partners or otherwise) are
antibodies. As is well known in the art, when using an antibody or
fragment thereof for therapeutic purposes it may prove advantageous
to use a "humanized" or "veneered" version of an antibody of
interest to reduce any potential immunogenic reaction. In general,
"humanized" or "veneered" antibody molecules and fragments thereof
minimize unwanted immunological responses toward antihuman antibody
molecules which can limit the duration and effectiveness of
therapeutic applications of those moieties in human recipients.
[0085] A number of "humanized" antibody molecules comprising an
antigen binding portion derived from a non-human immunoglobulin
have been described in the art, including chimeric antibodies
having rodent variable regions and their associated
complementarity-determining regions (CDRs) fused to human constant
domains (e.g., see Winter et al., Nature 349:293, 1991; Lobuglio et
al., Proc. Nat. Acad. Sci. USA 86:4220, 1989; Shaw et al., J.
Immunol. 138:4534, 1987; and Brown et al., Cancer Res. 47:3577,
1987), rodent CDRs grafted into a human supporting framework region
(FR) prior to fusion with an appropriate human antibody constant
domain (e.g., see Riechmann et al., Nature 332:323, 1988; Verhoeyen
et al., Science 239:1534, 1988; and Jones et al. Nature 321:522,
1986) and rodent CDRs supported by recombinantly veneered rodent
FRs (e.g., see European Patent Publication No. 519,596, published
Dec. 23, 1992). It is to be understood that the invention also
encompasses "fully human" antibodies produced using the
XenoMouse.TM. technology (AbGenix Corp., Fremont, Calif.) according
to the techniques described in U.S. Pat. No. 6,075,181.
[0086] Yet further, so-called "veneered" antibodies may be used
that include "veneered FRs". The process of veneering involves
selectively replacing FR residues from, e.g., a murine heavy or
light chain variable region, with human FR residues in order to
provide a xenogeneic molecule comprising an antigen binding portion
which retains substantially all of the native FR polypeptide
folding structure. Veneering techniques are based on the
understanding that the antigen binding characteristics of an
antigen binding portion are determined primarily by the structure
and relative disposition of the heavy and light chain CDR sets
within the antigen-association surface (e.g., see Davies et al.,
Ann. Rev. Biochem. 59:439, 1990). Thus, antigen association
specificity can be preserved in a humanized antibody only wherein
the CDR structures, their interaction with each other and their
interaction with the rest of the variable region domains are
carefully maintained. By using veneering techniques, exterior
(e.g., solvent-accessible) FR residues which are readily
encountered by the immune system are selectively replaced with
human residues to provide a hybrid molecule that comprises either a
weakly immunogenic, or substantially non-immunogenic veneered
surface.
[0087] Preferably, interaction partners suitable for use as
therapeutics (or therapeutic agent carriers) exhibit high
specificity for the target tumor marker and low background binding
to other tumor markers. In certain embodiments, monoclonal
antibodies are preferred for therapeutic purposes.
[0088] Tumor markers that are expressed on the cell surface
represent preferred targets for the development of therapeutic
agents, particularly therapeutic antibodies. For example, cell
surface proteins can be tentatively identified using sequence
analysis based on the presence of a predicted transmembrane domain.
Their presence on the cell surface can ultimately be confirmed
using IHC.
[0089] Kits
[0090] Useful sets or panels of interaction partners according to
the present invention may be prepared and packaged together in kits
for use in classifying, diagnosing, or otherwise characterizing
tumor samples, or for inhibiting tumor cell growth or otherwise
treating cancer.
[0091] Any available technique may be utilized in the preparation
of individual interaction partners for inclusion in kits. For
example, protein or polypeptide interaction partners may be
produced by cells (e.g., recombinantly or otherwise), may be
chemically synthesized, or may be otherwise generated in vitro
(e.g., via in vitro transcription and/or translation). Non-protein
or polypeptide interaction partners (e.g., small molecules, etc.)
may be synthesized, may be isolated from within or around cells
that produce them, or may be otherwise generated.
[0092] When antibodies are used as interaction partners, these may
be prepared by any of a variety of techniques known to those of
ordinary skill in the art (e.g., see Harlow and Lane, Antibodies: A
Laboratory Manual, Cold Spring Harbor Laboratory, 1988). For
example, antibodies can be produced by cell culture techniques,
including the generation of monoclonal antibodies, or via
transfection of antibody genes into suitable bacterial or mammalian
cell hosts, in order to allow for the production of recombinant
antibodies. In one technique, an "immunogen" comprising an
antigenic portion of a tumor marker of interest (or the tumor
marker itself) is initially injected into any of a wide variety of
mammals (e.g., mice, rats, rabbits, sheep or goats). In this step,
a tumor marker (or an antigenic portion thereof) may serve as the
immunogen without modification. Alternatively, particularly for
relatively short tumor markers, a superior immune response may be
elicited if the tumor marker is joined to a carrier protein, such
as bovine serum albumin or keyhole limpet hemocyanin (KLH). The
immunogen is injected into the animal host, preferably according to
a predetermined schedule incorporating one or more booster
immunizations and the animals are bled periodically. Polyclonal
antibodies specific for the tumor marker may then be purified from
such antisera by, for example, affinity chromatography using the
tumor marker (or an antigenic portion thereof) coupled to a
suitable solid support. An exemplary method is described in Example
7.
[0093] If desired for diagnostic or therapeutic kits, monoclonal
antibodies specific for a tumor marker of interest may be prepared,
for example, using the technique of Kohler and Milstein, Eur. J.
Immunol. 6:511, 1976 and improvements thereto. Briefly, these
methods involve the preparation of immortal cell lines capable of
producing antibodies having the desired specificity (i.e.,
reactivity with the tumor marker of interest). Such cell lines may
be produced, for example, from spleen cells obtained from an animal
immunized as described above. The spleen cells are then
immortalized by, for example, fusion with a myeloma cell fusion
partner, preferably one that is syngeneic with the immunized
animal. A variety of fusion techniques may be employed. For
example, the spleen cells and myeloma cells may be combined with a
nonionic detergent for a few minutes and then plated at low density
on a selective medium that supports the growth of hybrid cells, but
not myeloma cells. A preferred selection technique uses HAT
(hypoxanthine, aminopterin, thymidine) selection. After a
sufficient time, usually about 1 to 2 weeks, colonies of hybrids
are observed. Single colonies are selected and their culture
supernatants tested for binding activity against the tumor marker.
Hybridomas having high reactivity and specificity are
preferred.
[0094] Monoclonal antibodies may be isolated from the supernatants
of growing hybridoma colonies. In addition, various techniques may
be employed to enhance the yield, such as injection of the
hybridoma cell line into the peritoneal cavity of a suitable
vertebrate host, such as a mouse. Monoclonal antibodies may then be
harvested from the ascites fluid or the blood. Contaminants may be
removed from the antibodies by conventional techniques, such as
chromatography, gel filtration, precipitation and extraction. The
tumor marker of interest may be used in the purification process
in, for example, an affinity chromatography step.
[0095] In addition to inventive interaction partners, preferred
kits for use in accordance with the present invention may include,
a reference sample, instructions for processing samples, performing
the test, instructions for interpreting the results, buffers and/or
other reagents necessary for performing the test. In certain
embodiments the kit can comprise a panel of antibodies.
[0096] Pharmaceutical Compositions
[0097] As mentioned above, the present invention provides new
therapies and methods for identifying these. In certain
embodiments, an interaction partner may be a useful therapeutic
agent. Alternatively or additionally, interaction partners defined
or prepared according to the present invention bind to tumor
markers that serve as targets for therapeutic agents. Also,
inventive interaction partners may be used to deliver a therapeutic
agent to a cancer cell. For example, interaction partners provided
in accordance with the present invention may be coupled to one or
more therapeutic agents.
[0098] In addition, as mentioned above, to the extent that a
particular predictive panel correlates with responsiveness to a
particular therapy because it detects changes that reflect
inhibition (or inhibitability) of cancer cell growth, that panel
could be used to evaluate therapeutic candidates (e.g., small
molecule drugs) for their ability to induce the same or similar
changes in different cells. In particular, binding by the panel
could be assessed on cancer cells before and after exposure to
candidate therapeutics; those candidates that induce expression of
the tumor markers to which the panel binds are then identified.
[0099] The invention includes pharmaceutical compositions
comprising these inventive therapeutic agents. In general, a
pharmaceutical composition will include a therapeutic agent in
addition to one or more inactive agents such as a sterile,
biocompatible carrier including, but not limited to, sterile water,
saline, buffered saline, or dextrose solution. The pharmaceutical
compositions may be administered either alone or in combination
with other therapeutic agents including other chemotherapeutic
agents, hormones, vaccines and/or radiation therapy. By "in
combination with", it is not intended to imply that the agents must
be administered at the same time or formulated for delivery
together, although these methods of delivery are within the scope
of the invention. In general, each agent will be administered at a
dose and on a time schedule determined for that agent.
Additionally, the invention encompasses the delivery of the
inventive pharmaceutical compositions in combination with agents
that may improve their bioavailability, reduce or modify their
metabolism, inhibit their excretion, or modify their distribution
within the body. The invention encompasses treating cancer by
administering the pharmaceutical compositions of the invention.
Although the pharmaceutical compositions of the present invention
can be used for treatment of any subject (e.g., any animal) in need
thereof, they are most preferably used in the treatment of
humans.
[0100] The pharmaceutical compositions of this invention can be
administered to humans and other animals by a variety of routes
including oral, intravenous, intramuscular, intra-arterial,
subcutaneous, intraventricular, transdermal, rectal, intravaginal,
intraperitoneal, topical (as by powders, ointments, or drops),
bucal, or as an oral or nasal spray or aerosol. In general the most
appropriate route of administration will depend upon a variety of
factors including the nature of the agent (e.g., its stability in
the environment of the gastrointestinal tract), the condition of
the patient (e.g., whether the patient is able to tolerate oral
administration), etc. At present the intravenous route is most
commonly used to deliver therapeutic antibodies. However, the
invention encompasses the delivery of the inventive pharmaceutical
composition by any appropriate route taking into consideration
likely advances in the sciences of drug delivery.
[0101] General considerations in the formulation and manufacture of
pharmaceutical agents may be found, for example, in Remington's
Pharmaceutical Sciences, 19.sup.th ed., Mack Publishing Co.,
Easton, Pa., 1995.
[0102] According to the methods of treatment of the present
invention, cancer is treated or prevented in a patient such as a
human or other mammal by administering to the patient a
therapeutically effective amount of a therapeutic agent of the
invention, in such amounts and for such time as is necessary to
achieve the desired result. By a "therapeutically effective amount"
of a therapeutic agent of the invention is meant a sufficient
amount of the therapeutic agent to treat (e.g., to ameliorate the
symptoms of, delay progression of, prevent recurrence of, cure,
etc.) cancer at a reasonable benefit/risk ratio, which involves a
balancing of the efficacy and toxicity of the therapeutic agent. In
general, therapeutic efficacy and toxicity may be determined by
standard pharmacological procedures in cell cultures or with
experimental animals, e.g., by calculating the ED.sub.50 (the dose
that is therapeutically effective in 50% of the treated subjects)
and the LD.sub.50 (the dose that is lethal to 50% of treated
subjects). The ED.sub.50/LD.sub.50 represents the therapeutic index
of the agent. Although in general therapeutic agents having a large
therapeutic index are preferred, as is well known in the art, a
smaller therapeutic index may be acceptable in the case of a
serious disease, particularly in the absence of alternative
therapeutic options. Ultimate selection of an appropriate range of
doses for administration to humans is determined in the course of
clinical trials.
[0103] It will be understood that the total daily usage of the
therapeutic agents and compositions of the present invention for
any given patient will be decided by the attending physician within
the scope of sound medical judgment. The specific therapeutically
effective dose level for any particular patient will depend upon a
variety of factors including the disorder being treated and the
severity of the disorder; the activity of the specific therapeutic
agent employed; the specific composition employed; the age, body
weight, general health, sex and diet of the patient; the time of
administration, route of administration and rate of excretion of
the specific therapeutic agent employed; the duration of the
treatment; drugs used in combination or coincidental with the
specific therapeutic agent employed; and like factors well known in
the medical arts.
[0104] The total daily dose of the therapeutic agents of this
invention administered to a human or other mammal in single or in
divided doses can be in amounts, for example, from 0.01 to 50 mg/kg
body weight or more usually from 0.1 to 25 mg/kg body weight.
Single dose compositions may contain such amounts or submultiples
thereof to make up the daily dose. In general, treatment regimens
according to the present invention comprise administration to a
patient in need of such treatment from about 0.1 .mu.g to about
2000 mg of the therapeutic agent(s) of the invention per day in
single or multiple doses.
EXEMPLIFICATION
Example 1
Selection of Candidate Genes and Identification of Potential
Interaction Partners for Tumor Classification Panels
[0105] The present inventors identified a collection of candidate
genes that (a) were differentially expressed across a set of tumor
samples in a manner that suggested they distinguish biologically
distinct classes of tumors; (b) were members of a gene functional
class that has been linked to cellular pathways implicated in tumor
prognosis or drug resistance; (c) were known or thought to display
an expression, localization, modification, or activity pattern that
correlates with a relevant tumor feature; etc.
[0106] For example, differentially expressed genes were identified
using microarrays as described in co-pending U.S. patent
application Ser. No. 09/916,722, filed Jul. 26, 2001 entitled
"REAGENTS AND METHODS FOR USE IN MANAGING BREAST CANCER", the
entire contents of which are incorporated herein by reference.
Other genes were typically selected on the basis of published data
suggesting their possible implication in drug resistance, cancer
prognosis, etc. A total of 730 candidate genes were identified as
encoding proteins against which antibodies should be raised.
[0107] Rabbit polyclonal affinity-purified antibodies were then
raised against 661 of these proteins as described in Example 7.
Each antibody was initially tested over a range of dilutions on
tissue arrays that included a set of normal tissues, tumor tissues
and cell lines, so that, for each antibody, a discriminating titer
was established at which differential staining across the diverse
set was observed. The preparation and staining of tissue arrays is
described in greater detail in Example 8. Of the 661 antibodies
subjected to this analysis, 460 showed differential staining and
were considered of sufficient interest for further analysis.
Example 2
Breast Cancer Classification Panel (Russian Breast Cohort)
[0108] The present inventors prepared an exemplary panel of
antibodies for use in classifying breast tumors. 272 of the 460
differentially staining antibodies of Example 1 exhibited a
reproducibly robust staining pattern on tissues relevant for this
application. These antibodies were therefore applied (at
appropriate titers) to a tissue array comprised of approximately
400 independent breast tumor samples from a cohort of breast cancer
patients (the Russian breast cohort). Stained tissue samples were
scored by a trained cytotechnologist or pathologist on a
semi-quantitative scale in which 0=no stain on tumor cells; 1=no
information; 2=weak staining of tumor cells; and 3=strong staining
of tumor cells. Antibodies were included in a breast cancer
classification panel if they stained greater than 10% and less than
90% of a defined "consensus panel" of the breast tumor tissue
samples on at least two independent tissue arrays.
[0109] A given tissue sample was included in this "consensus panel"
if at least 80% of the antibodies tested gave interpretable scores
(i.e., a non-zero score) with that sample. Of the 400 breast tumor
samples in the tissue array about 320 were included in the
consensus panel. Also, in scoring antibody binding to the consensus
panel, all scores represented a consensus score of replicate tissue
arrays comprised of independent samples from the same sources. The
consensus score was determined by computing the median (rounded
down to an integer, where applicable) of all scores associated with
a given antibody applied under identical conditions to the
particular patient sample. In cases where the variance of the
scores was greater than 2, the score was changed to 1 (i.e., no
information). The data for each antibody was stored in an
Oracle-based database that contained the semi-quantitative scores
of tumor tissue staining and also contained links to both patient
clinical information and stored images of the stained patient
samples.
[0110] Through this analysis 90 of the 272 tested antibodies were
selected for inclusion in an exemplary breast cancer classification
panel (see Appendix A, e.g., S0021, S0022, S0039, etc.). It is to
be understood that any sub-combination of these 90 antibodies may
be used in constructing an inventive breast cancer classification
panel. It will also be appreciated that additional antibodies may
be added to or removed from an inventive breast cancer
classification panel as more tumor markers are identified and/or
more samples are tested (e.g., see Example 3).
[0111] FIG. 1 shows the pattern of reactivity observed with certain
members of this panel of antibodies across samples from the Russian
breast cohort. Dark gray represents strong positive staining, black
represents weak positive staining, while light gray represents the
absence of staining and medium gray represents a lack of data.
Images of stained samples can be found in Appendix B (see right
hand column of Appendix A for cross-references to corresponding
antibodies).
[0112] The patients (rows) were classified using k-means clustering
(as described, for example, in MacQueen in Proceedings of the Fifth
Berkeley Symposium on Mathematical Statistics and Probability (Le
Cam et al., Eds.; University of California Press, Berkeley, Calif.)
1:281, 1967; Heyer et al., Genome Res. 9:1106, 1999, each of which
is incorporated herein by reference) while the antibodies (columns)
were organized using hierarchical clustering (as described in, for
example, Sokal et al., Principles of Numerical Tazonomy (Freeman
& Co., San Francisco, Calif.), 1963; Eisen et al., Proc. Natl.
Acad. Sci. USA 95:14863, 1998, each of which is incorporated herein
by reference). As shown in FIG. 1, nine sub-classes of breast
cancer patients were identified by their consensus pattern of
staining with this breast cancer classification panel.
Example 3
Breast Cancer Classification Panel (HH Breast Cohort)
[0113] In order to refine and expand the breast cancer
classification panel of Example 2, the present inventors tested 109
of the 460 differentially staining antibodies of Example 1 on
samples from a new cohort of 550 breast cancer patients (the
Huntsville Hospital breast cohort or "HH breast" cohort, the
characteristics of which are described in Example 10).
[0114] Antibodies were included in an updated breast cancer
classification panel if they stained more than 10% and less than
90% of the particular consensus panel of tissue samples tested.
Through this analysis 87 of the 109 tested antibodies were selected
(see Appendix A, e.g., S0011, S0018, S0020, etc.).
Example 4
Lung Cancer Classification Panel (Russian Lung Cohort)
[0115] The present inventors also prepared an exemplary panel of
antibodies for use in classifying lung tumors. 417 of the 460
differentially staining antibodies of Example 1 exhibited a
reproducibly robust staining pattern on tissues relevant for this
application. These antibodies were therefore applied (at the titers
determined in Example 1) to a tissue array comprised of
approximately 400 independent lung tumor tissues from a cohort of
lung cancer patients (the Russian lung cohort). Stained tissue
samples were scored by a trained cytotechnologist or pathologist as
before and again antibodies were included in the classification
panel if they stained greater than 10% and less than 90% of a
defined "consensus panel" of tissue samples on at least two
independent tissue arrays.
[0116] Through this analysis an exemplary lung cancer
classification panel was generated that was made up of 106 of the
417 tested antibodies (see Appendix A, e.g., S0021, S0022, S0024,
etc.). It is to be understood that any sub-combination of these 106
antibodies may be used in constructing an inventive lung cancer
classification panel. It will also be appreciated that additional
antibodies may be added to or removed from an inventive lung cancer
classification panel as more tumor markers are identified and/or
more samples are tested (e.g., see Example 5).
[0117] FIG. 2 shows the pattern of reactivity observed with certain
members of this panel of antibodies across samples from the Russian
lung cohort. Dark gray represents strong positive staining, black
represents weak positive staining, while light gray represents the
absence of staining and medium gray represents a lack of data.
Images of stained samples can be found in Appendix B (see right
hand column of Appendix A for cross-references to corresponding
antibodies).
[0118] The patients (rows) were again classified using k-means
clustering while the antibodies (columns) were organized using
hierarchical clustering. As shown in FIG. 2, eight sub-classes of
lung cancer patients were identified by their consensus pattern of
staining with this lung cancer classification panel.
Example 5
Lung Cancer Classification Panel (HH Lung Cohort)
[0119] In order to refine and expand the lung cancer classification
panel of Example 4, the present inventors tested 54 of the 460
differentially staining antibodies of Example 1 on samples from a
new cohort of 379 lung cancer patients (the Huntsville Hospital
lung cohort or "HH lung" cohort, the characteristics of which are
described in Example 11).
[0120] Antibodies were included in an updated colon cancer
classification panel if they stained more than 10% and less than
90% of the particular consensus panel of tissue samples tested.
Through this analysis 39 of the 54 tested antibodies were selected
(see Appendix A, e.g., S0021, S0022, S0046, etc.).
Example 6
Colon Cancer Classification Panel (Russian Colon Cohort)
[0121] The present inventors also prepared an exemplary panel of
antibodies for use in classifying colon tumors. 382 of the 460
differentially staining antibodies of Example 1 exhibited a
reproducibly robust staining pattern on tissues relevant for this
application. These antibodies were therefore applied (at the titers
determined in Example 1) to a tissue array comprised of
approximately 400 independent colon tumor tissues from a cohort of
colon cancer patients (the Russian colon cohort). Stained tissue
samples were scored by a trained cytotechnologist or pathologist as
before and again antibodies were included in the classification
panel if they stained greater than 10% and less than 90% of a
defined "consensus panel" of tissue samples on at least two
independent tissue arrays.
[0122] Through this analysis a colon antibody classification panel
was generated that was made up of 86 of the 382 tested antibodies
(see Appendix A, e.g., S0022, S0036, S0039, etc.). It will be
appreciated that any sub-combination of these 86 antibodies may be
used in constructing an inventive colon cancer classification
panel. It will also be appreciated that additional antibodies may
be added to or removed from an inventive colon cancer
classification panel as more tumor markers are identified and/or
more samples are tested.
[0123] FIG. 3 shows the pattern of reactivity observed with certain
members of this panel of antibodies across samples from the Russian
colon cohort. Dark gray represents strong positive staining, black
represents weak positive staining, while light gray represents the
absence of staining and medium gray represents a lack of data.
Images of the stained samples can be found in Appendix B (see right
hand column of Appendix A for cross-references to corresponding
antibodies).
[0124] The patients (rows) were again classified using k-means
clustering while the antibodies (columns) were organized using
hierarchical clustering. As shown in FIG. 3, seven sub-classes of
patients were identified by their consensus pattern of staining
with this exemplary colon cancer classification panel.
Example 7
Raising Antibodies
[0125] This example describes a method that was employed to
generate the majority of the antibodies that were used in Examples
1-6. Similar methods may be used to generate an antibody that binds
to any polypeptide of interest (e.g., to polypeptides that are or
are derived from other tumor markers). In some cases, antibodies
may be obtained from commercial sources (e.g., Chemicon, Dako,
Oncogene Research Products, NeoMarkers, etc.) or other publicly
available sources (e.g., Imperial Cancer Research Technology,
etc.).
[0126] Materials and Solutions
[0127] Anisole (Cat. No. A4405, Sigma, St. Louis, Mo.)
[0128] 2,2'-azino-di-(3-ethyl-benzthiazoline-sulfonic acid) (ABTS)
(Cat. No. A6499, Molecular Probes, Eugene, Oreg.)
[0129] Activated maleimide Keyhole Limpet Hemocyanin (Cat. No.
77106, Pierce, Rockford, Ill.)
[0130] Keyhole Limpet Hemocyanin (Cat. No. 77600, Pierce, Rockford,
Ill.)
[0131] Phosphoric Acid (H.sub.3PO.sub.4) (Cat. No. P6560,
Sigma)
[0132] Glacial Acetic Acid (Cat No. BP1185-500, Fisher)
[0133] EDC (EDAC) (Cat No. 341006, Calbiochem)
[0134] 25% Glutaraldehyde (Cat No. G-5882, Sigma)
[0135] Glycine (Cat No. G-8898, Sigma)
[0136] Biotin (Cat. No. B2643, Sigma)
[0137] Boric acid (Cat. No. B0252, Sigma)
[0138] Sepharose 4B (Cat. No. 17-0120-01, LKB/Pharmacia, Uppsala,
Sweden)
[0139] Bovine Serum Albumin (LP) (Cat. No. 100 350, Boehringer
Mannheim, Indianapolis, Ind.)
[0140] Cyanogen bromide (Cat. No. C6388, Sigma)
[0141] Dialysis tubing Spectra/Por Membrane MWCO: 6-8,000 (Cat. No.
132 665, Spectrum Industries, Laguna Hills, Calif.)
[0142] Dimethyl formamide (DMF) (Cat. No. 22705-6, Aldrich,
Milwaukee, Wis.)
[0143] DIC (Cat. No. BP 592-500, Fisher)
[0144] Ethanedithiol (Cat. No. 39,802-0, Aldrich)
[0145] Ether (Cat. No. TX 1275-3, EM Sciences)
[0146] Ethylenediaminetetraacetatic acid (EDTA) (Cat. No. BP 120-1,
Fisher, Springfield, N.J.)
[0147] 1-ethyl-3-(3'dimethylaminopropyl)-carbodiimide, HCL (EDC)
(Cat. no. 341-006, Calbiochem, San Diego, Calif.)
[0148] Freund's Adjuvant, complete (Cat. No. M-0638-50B, Lee
Laboratories, Grayson, Ga.)
[0149] Freund's Adjuvant, incomplete (Cat. No. M-0639-50B, Lee
Laboratories)
[0150] Fritted chromatography columns (Column part No. 12131011;
Frit Part No. 12131029, Varian Sample Preparation Products, Harbor
City, Calif.)
[0151] Gelatin from Bovine Skin (Cat. No. G9382, Sigma)
[0152] Goat anti-rabbit IgG, biotinylated (Cat. No. A 0418,
Sigma)
[0153] HOBt (Cat. No. 01-62-0008, Calbiochem)
[0154] Horseradish peroxidase (HRP) (Cat. No. 814 393, Boehringer
Mannheim)
[0155] HRP-Streptavidin (Cat. No. S 5512, Sigma)
[0156] Hydrochloric Acid (Cat. No. 71445-500, Fisher)
[0157] Hydrogen Peroxide 30% w/w (Cat. No. H1009, Sigma)
[0158] Methanol (Cat. No. A412-20, Fisher)
[0159] Microtiter plates, 96 well (Cat. No. 2595, Corning-Costar,
Pleasanton, Calif.)
[0160] N-.alpha.-Fmoc protected amino acids from Calbiochem. See
'97-'98 Catalog pp. 1-45.
[0161] N-.alpha.-Fmoc protected amino acids attached to Wang Resin
from Calbiochem. See '97-'98 Catalog pp. 161-164.
[0162] NMP (Cat. No. CAS 872-50-4, Burdick and Jackson, Muskegon,
Mich.)
[0163] Peptide (Synthesized by Research Genetics. Details given
below)
[0164] Piperidine (Cat. No. 80640, Fluka, available through
Sigma)
[0165] Sodium Bicarbonate (Cat. No. BP328-1, Fisher)
[0166] Sodium Borate (Cat. No. B9876, Sigma)
[0167] Sodium Carbonate (Cat. No. BP357-1, Fisher)
[0168] Sodium Chloride (Cat. No. BP 358-10, Fisher)
[0169] Sodium Hydroxide (Cat. No. SS 255-1, Fisher)
[0170] Streptavidin (Cat. No. 1 520, Boehringer Mannheim)
[0171] Thioanisole (Cat. No. T-2765, Sigma)
[0172] Trifluoroacetic acid (Cat. No. TX 1275-3, EM Sciences)
[0173] Tween-20 (Cat. No. BP 337-500, Fisher)
[0174] Wetbox (Rectangular Servin' Saver.TM. Part No. 3862,
Rubbermaid, Wooster, Ohio)
[0175] BBS--Borate Buffered Saline with EDTA dissolved in distilled
water (pH 8.2 to 8.4 with HCl or NaOH), 25 mM Sodium borate
(Borax), 100 mM Boric Acid, 75 mM NaCl and 5 mM EDTA.
[0176] 0.1 N HCl in saline as follows: concentrated HCl (8.3
ml/0.917 liter distilled water) and 0.154 M NaCl
[0177] Glycine (pH 2.0 and pH 3.0) dissolved in distilled water and
adjusted to the desired pH, 0.1 M glycine and 0.154 M NaCl.
[0178] 5.times. Borate 1.times. Sodium Chloride dissolved in
distilled water, 0.11 M NaCl, 60 mM Sodium Borate and 250 mM Boric
Acid.
[0179] Substrate Buffer in distilled water adjusted to pH 4.0 with
sodium hydroxide, 50 to 100 mM Citric Acid.
[0180] AA solution: HOBt is dissolved in NMP (8.8 grams HOBt to 1
liter NMP). Fmoc-N-a-amino at a concentration at 0.53 M.
[0181] DIC solution: 1 part DIC to 3 parts NMP.
[0182] Deprotecting solution: 1 part Piperidine to 3 parts DMF.
[0183] Reagent R: 2 parts anisole, 3 parts ethanedithiol, 5 parts
thioanisole and 90 parts trifluoroacetic acid.
[0184] Equipment
[0185] MRX Plate Reader (Dynatech, Chantilly, Va.)
[0186] Hamilton Eclipse (Hamilton Instruments, Reno, Nev.)
[0187] Beckman TJ-6 Centrifuge (Model No. TJ-6, Beckman
Instruments, Fullerton, Calif.)
[0188] Chart Recorder (Recorder 1 Part No. 18-1001-40, Pharmacia
LKB Biotechnology)
[0189] UV Monitor (Uvicord SII Part No. 18-1004-50, Pharmacia LKB
Biotechnology)
[0190] Amicon Stirred Cell Concentrator (Model 8400, Amicon,
Beverly, Mass.)
[0191] 30 kD MW cut-off filter (Cat. No. YM-30 Membranes Cat. No.
13742, Amicon)
[0192] Multi-channel Automated Pipettor (Cat. No. 4880, Corning
Costar, Cambridge, Mass.)
[0193] pH Meter Corning 240 (Corning Science Products, Corning
Glassworks, Corning, N.Y.)
[0194] ACT396 peptide synthesizer (Advanced ChemTech, Louisville,
Ky.)
[0195] Vacuum dryer (Box from Labconco, Kansas City, Mo. and Pump
from Alcatel, Laurel, Md.).
[0196] Lyophilizer (Unitop 600 sl in tandem with Freezemobile 12,
both from Virtis, Gardiner, N.Y.)
[0197] Peptide Selection
[0198] Peptides against which antibodies would be raised were
selected from within the polypeptide sequence of interest using a
program that uses the Hopp/Woods method (described in Hopp and
Woods, Mol. Immunol. 20:483, 1983 and Hopp and Woods, Proc. Nat.
Acad. Sci. U.S.A. 78:3824, 1981). The program uses a scanning
window that identifies peptide sequences of 15-20 amino acids
containing several putative antigenic epitopes as predicted by low
solvent accessibility. This is in contrast to most implementations
of the Hopp/Woods method, which identify single short (.about.6
amino acids) presumptive antigenic epitopes. Occasionally the
predicted solvent accessibility was further assessed by PHD
prediction of loop structures (described in Rost and Sander,
Proteins 20:216, 1994). Preferred peptide sequences display minimal
similarity with additional known human proteins. Similarity was
determined by performing BLASTP alignments, using a wordsize of 2
(described in Altschul et al., J. Mol. Biol. 215:403, 1990). All
alignments given an EXPECT value less than 1000 were examined and
alignments with similarities of greater than 60% or more than four
residues in an exact contiguous non-gapped alignment forced those
peptides to be rejected. When it was desired to target regions of
proteins exposed outside the cell membrane, extracellular regions
of the protein of interest were determined from the literature or
as defined by predicted transmembrane domains using a hidden Markov
model (described in Krogh et al., J. Mol. Biol. 305:567, 2001).
When the peptide sequence was in an extracellular domain, peptides
were rejected if they contained N-linked glycosylation sites. As
shown in Appendix A, one to three peptide sequences were selected
for each polypeptide using this procedure.
[0199] Peptide Synthesis
[0200] The sequence of the desired peptide was provided to the
peptide synthesizer. The C-terminal residue was determined and the
appropriate Wang Resin was attached to the reaction vessel. The
peptides were synthesized C-terminus to N-terminus by adding one
amino acid at a time using a synthesis cycle. Which amino acid is
added was controlled by the peptide synthesizer, which looks to the
sequence of the peptide that was entered into its database. The
synthesis steps were performed as follows:
[0201] Step 1--Resin Swelling: Added 2 ml DMF, incubated 30
minutes, drained DMF.
[0202] Step 2--Synthesis cycle (repeated over the length of the
peptide)
[0203] 2a--Deprotection: 1 ml deprotecting solution was added to
the reaction vessel and incubated for 20 minutes.
[0204] 2b--Wash Cycle
[0205] 2c--Coupling: 750 ml of amino acid solution (changed as the
sequence listed in the peptide synthesizer dictated) and 250 ml of
DIC solution were added to the reaction vessel. The reaction vessel
was incubated for thirty minutes and washed once. The coupling step
was repeated once.
[0206] 2d--Wash Cycle
[0207] Step 3--Final Deprotection: Steps 2a and 2b were performed
one last time.
[0208] Resins were deswelled in methanol (rinsed twice in 5 ml
methanol, incubated 5 minutes in 5 ml methanol, rinsed in 5 ml
methanol) and then vacuum dried.
[0209] Peptide was removed from the resin by incubating 2 hours in
reagent R and then precipitated into ether. Peptide was washed in
ether and then vacuum dried. Peptide was resolubilized in
diH.sub.2O, frozen and lyophilized overnight.
[0210] Conjugation of Peptide with Keyhole Limpet Hemocyanin
[0211] Peptide (6 mg) was conjugated with Keyhole Limpet Hemocyanin
(KLH). When the selected peptide included at least one cysteine,
three aliquots (2 mg) were dissolved in PBS (2 ml) and coupled to
KLH via glutaraldehyde, EDC or maleimide activated KLH (2 mg) in 2
ml of PBS for a total volume of 4 ml. When the peptide lacked
cysteine, two aliquots (3 mg) were coupled via glutaraldehyde and
EDC methods.
[0212] Maleimide coupling is accomplished by mixing 2 mg of peptide
with 2 mg of maleimide-activated KLH dissolved in PBS (4 ml) and
incubating 4 hr.
[0213] EDC coupling is accomplished by mixing 2 mg of peptide, 2 mg
unmodified KLH, and 20 mg of EDC in 4 ml PBS (lowered to pH 5 by
the addition of phosphoric acid), and incubating for 4 hours. The
reaction is stopped by the slow addition of 1.33 ml acetic acid (pH
4.2). When using EDC to couple 3 mg of peptide, the amounts listed
above are increased by a factor of 1.5.
[0214] Glutaraldehyde coupling occurs when 2 mg of peptide are
mixed with 2 mg of KLH in 0.9 ml of PBS. 0.9 ml of 0.2%
glutaraldehyde in PBS is added and mixed for one hour. 0.46 ml of 1
M glycine in PBS is added and mixed for one hour. When using
glutaraldehyde to couple 3 mg of peptide, the above amounts are
increased by a factor of 1.5.
[0215] The conjugated aliquots were subsequently repooled, mixed
for two hours, dialyzed in 1 liter PBS and lyophilized.
[0216] Immunization of Rabbits
[0217] Two New Zealand White Rabbits were injected with 250 .mu.g
(total) KLH conjugated peptide in an equal volume of complete
Freund's adjuvant and saline in a total volume of 1 ml. 100 .mu.g
KLH conjugated peptide in an equal volume of incomplete Freund's
Adjuvant and saline were then injected into three to four
subcutaneous dorsal sites for a total volume of 1 ml two, six,
eight and twelve weeks after the first immunization. The
immunization schedule was as follows:
1 Day 0 Pre-immune bleed, primary immunization Day 15 1st boost Day
27 1st bleed Day 44 2nd boost Day 57 2nd bleed and 3rd boost Day 69
3rd bleed Day 84 4th boost Day 98 4th bleed
[0218] Collection of Rabbit Serum
[0219] The rabbits were bled (30 to 50 ml) from the auricular
artery. The blood was allowed to clot at room temperature for 15
minutes and the serum was separated from the clot using an IEC
DPR-6000 centrifuge at 5000 g. Cell-free serum was decanted gently
into a clean test tube and stored at -20.degree. C. for affinity
purification.
[0220] Determination of Antibody Titer
[0221] All solutions with the exception of wash solution were added
by the Hamilton Eclipse, a liquid handling dispenser. The antibody
titer was determined in the rabbits using an ELISA assay with
peptide on the solid phase. Flexible high binding ELISA plates were
passively coated with peptide diluted in BBS (100 .mu., 1
.mu.g/well) and the plate was incubated at 4.degree. C. in a wetbox
overnight (air-tight container with moistened cotton balls). The
plates were emptied and then washed three times with BBS containing
0.1% Tween-20 (BBS-TW) by repeated filling and emptying using a
semi-automated plate washer. The plates were blocked by completely
filling each well with BBS-TW containing 1% BSA and 0.1% gelatin
(BBS-TW-BG) and incubating for 2 hours at room temperature. The
plates were emptied and sera of both pre- and post-immune serum
were added to wells. The first well contained sera at 1:50 in BBS.
The sera were then serially titrated eleven more times across the
plate at a ratio of 1:1 for a final (twelfth) dilution of
1:204,800. The plates were incubated overnight at 4.degree. C. The
plates were emptied and washed three times as described.
[0222] Biotinylated goat anti-rabbit IgG (100 .mu.l) was added to
each microtiter plate test well and incubated for four hours at
room temperature. The plates were emptied and washed three times.
Horseradish peroxidase-conjugated Streptavidin (100 .mu.l diluted
1:10,000 in BBS-TW-BG) was added to each well and incubated for two
hours at room temperature. The plates were emptied and washed three
times. The ABTS was prepared fresh from stock by combining 10 ml of
citrate buffer (0.1 M at pH 4.0), 0.2 ml of the stock solution (15
mg/ml in water) and 10 .mu.l of 30% hydrogen peroxide. The ABTS
solution (100 .mu.l) was added to each well and incubated at room
temperature. The plates were read at 414 nm, 20 minutes following
the addition of substrate.
[0223] Preparation of Peptide Affinity Purification Column:
[0224] The affinity column was prepared by conjugating 5 mg of
peptide to 10 ml of cyanogen bromide-activated Sepharose 4B and 5
mg of peptide to hydrazine-Sepharose 4B. Briefly, 100 .mu.l of DMF
was added to peptide (5 mg) and the mixture was vortexed until the
contents were completely wetted. Water was then added (900 .mu.l)
and the contents were vortexed until the peptide dissolved. Half of
the dissolved peptide (500 .mu.l) was added to separate tubes
containing 10 ml of cyanogen-bromide activated Sepharose 4B in 0.1
ml of borate buffered saline at pH 8.4 (BBS) and 10 ml of
hydrazine-Sepharose 4B in 0.1 M carbonate buffer adjusted to pH 4.5
using excess EDC in citrate buffer pH 6.0. The conjugation
reactions were allowed to proceed overnight at room temperature.
The conjugated Sepharose was pooled and loaded onto fritted
columns, washed with 10 ml of BBS, blocked with 10 ml of 1 M
glycine and washed with 10 ml 0.1 M glycine adjusted to pH 2.5 with
HCl and re-neutralized in BBS. The column was washed with enough
volume for the optical density at 280 m to reach baseline.
[0225] Affinity Purification of Antibodies
[0226] The peptide affinity column was attached to a UV monitor and
chart recorder. The titered rabbit antiserum was thawed and pooled.
The serum was diluted with one volume of BBS and allowed to flow
through the columns at 10 ml per minute. The non-peptide
immunoglobulins and other proteins were washed from the column with
excess BBS until the optical density at 280 nm reached baseline.
The columns were disconnected and the affinity purified column was
eluted using a stepwise pH gradient from pH 7.0 to 1.0. The elution
was monitored at 280 nm and fractions containing antibody (pH 3.0
to 1.0) were collected directly into excess 0.5 M BBS. Excess
buffer (0.5 M BBS) in the collection tubes served to neutralize the
antibodies collected in the acidic fractions of the pH
gradient.
[0227] The entire procedure was repeated with "depleted" serum to
ensure maximal recovery of antibodies. The eluted material was
concentrated using a stirred cell apparatus and a membrane with a
molecular weight cutoff of 30 kD. The concentration of the final
preparation was determined using an optical density reading at 280
nm. The concentration was determined using the following formula:
mg/ml=OD.sub.280/1.4.
[0228] It will be appreciated that in certain embodiments,
additional steps may be used to purify antibodies of the invention.
In particular, it may prove advantageous to repurify antibodies,
e.g., against one of the peptides that was used in generating the
antibodies. It is to be understood that the present invention
encompasses antibodies that have been prepared with such additional
purification or repurification steps. It will also be appreciated
that the purification process may affect the binding between
samples and the inventive antibodies.
Example 8
Preparing and Staining Tissue Arrays
[0229] This example describes a method that was employed to prepare
the tissue arrays that were used in Examples 1-6. This example also
describes how the antibody staining was performed.
[0230] Tissue arrays were prepared by inserting full-thickness
cores from a large number of paraffin blocks (donor blocks) that
contain fragments of tissue derived from many different patients
and/or different tissues or fragments of tissues from a single
patient, into a virgin paraffin block (recipient block) in a grid
pattern at designated locations in a grid. A standard slide of the
paraffin embedded tissue (donor block) was then made which
contained a thin section of the specimen amenable to H & E
staining. A trained pathologist, or the equivalent versed in
evaluating tumor and normal tissue, designated the region of
interest for sampling on the tissue array (e.g., a tumor area as
opposed to stroma). A commercially available tissue arrayer from
Beecher Instruments was then used to remove a core from the donor
block which was then inserted into the recipient block at a
designated location. The process was repeated until all donor
blocks had been inserted into the recipient block. The recipient
block was then thin-sectioned to yield 50-300 slides containing
cores from all cases inserted into the block.
[0231] The selected antibodies were then used to perform
immunohistochemical staining using the DAKO Envision+, Peroxidase
IHC kit (DAKO Corp., Carpenteria, Calif.) with DAB substrate
according to the manufacturer's instructions.
Example 9
Correlating Interaction Partner Binding with Outcome/Responsiveness
of Xenograft Tumors
[0232] According to the present invention, panels of useful
interaction partners may be defined through analysis of human tumor
cells grown in a non-human host. In particular, such analyses may
define interaction partner panels whose binding correlates with
prognosis and/or with responsiveness to therapy.
[0233] Cells derived from human tumors may be transplanted into a
host animal (e.g., a mouse), preferably into an immunocompromised
host animal. In preferred embodiments of the invention, cells
(e.g., cell lines, tumor samples obtained from human patients,
etc.) from a variety of different human tumors (e.g., at least 10,
20, 30, 40, 50, 60 or more different tumors) are transplanted into
host animals. The animals are then treated with different (e.g.,
increasing) concentrations of a chemical compound known or thought
to be selectively toxic to tumors with a predetermined common
characteristic (e.g., class or subclass). Relative growth or
regression of the tumors may then be assessed using standard
techniques.
[0234] In certain embodiments of the invention, a dataset of
sensitivity of the transplanted cells to a given compound or set of
compounds may optionally be created. For example, a dataset might
consist of the concentration of compound administered to the host
animal that inhibited tumor growth 50% at 96 hr (i.e., the
LD.sub.50) for each of the cell samples or cell lines tested. Such
a dataset, for example across at least 10, 20, 30, 40, 50, 60 or
more cell lines, could then be correlated with the relative
staining of the binding partners across the same cell lines. Those
binding partners whose interaction (or lack thereof) with cells was
highly correlated with either sensitivity to or resistance to a
given compound would be useful members of a predictive panel.
Example 10
Correlating Interaction Partner Binding with Clinical Prognostic
Data in Breast Cancer
[0235] According to the present invention, panels of useful
interaction partners may be defined through analysis of
correlations between binding patterns and clinical prognostic data.
In particular, such analyses may define interaction partner panels
whose binding correlates with prognosis.
[0236] The following describes the identification of exemplary
panels of antibodies whose binding has been shown to correlate with
the prognosis of breast cancer patients. The data was obtained
using samples from the Huntsville Hospital breast cohort (the "HH
breast" cohort) that was referred to in Example 3.
[0237] The HH breast cohort was generated from 1082 breast cancer
patients that were treated by the Comprehensive Cancer Institute
(Huntsville, Ala.) between 1990 and 2000. This larger group was
filtered to a study group of 550 patients by eliminating patients
according to the following criteria: 249 that had no chart which
could be found; 103 that had no clinical follow up; and 180 that
did not have sufficient clinical material in the paraffin block to
sample. For the remaining 550 patients, clinical data through Dec.
31, 2002 was available. Every patient in the cohort therefore had
between 2 and 13 years of follow-up. The average time of follow-up
among patients who did not recur was 5.6 years. Of the 550
patients, 140 had a recurrence of cancer within the study period;
353 patients were estrogen receptor positive (ER+); 154 were
estrogen receptor negative (ER--); and 43 were undetermined. Some
patients within these groups received adjuvant hormone therapy as
shown in Table 1:
2 TABLE 1 Total Hormone No hormone Unknown ER+ 353 278 68 7 ER- 154
70 83 1 Undetermined 43 28 15 0
[0238] In addition, 263 patients received chemotherapy. Up to 16
different regimens were used, however, most were variants of
cyclophosphamide, doxorubicin (with and without 5-fluorouracil
and/or cyclophosphamide), methotrexate and 5-fluorouracil. Finally,
333 of the patients received radiation. Clinical information
regarding age, stage, node status, tumor size, and grade was
obtained.
[0239] The clinical information for the patients in the cohort is
summarized in Table 2.
3 TABLE 2 All (550) ER+ (353) ER- (154) Stage = 1 236 162 49 Stage
= 2 269 167 87 Stage = 3 44 23 18 Undetermined 1 0 0 Mean Age @ Dx
58 59 55 Tumor status = 0 1 0 1 Tumor status = 1 295 203 63 Tumor
status = 2 195 122 62 Tumor status = 3 26 14 11 Tumor status = 4 14
6 8 Undetermined 21 8 9 Node status = 0 326 215 76 Node status = 1
205 127 71 Node status = 2 10 6 3 Undetermined 10 5 4 Metastasis =
0 527 338 147 Metastasis = 1 5 4 1 Undetermined 19 11 6
[0240] Where each category is defined in Table 3. These rules are
not fixed and staging is typically done by an oncologist based on
TNM status and other factors. These definitions for staging will
not necessarily match with the stage that each patient was actually
given. Node status is the primary tool for staging purposes.
4TABLE 3 Tumor status = 0 No evidence of tumor Tumor status = 1
<2 cm Tumor status = 2 2-5 cm Tumor status = 3 >5 cm Tumor
status = 4 Any size but extends to chest wall Node status = 0 No
regional LN metastasis Node status = 1 Ancillary LN metastasis but
nodes still moveable Node status = 2 Ancillary LN metastasis with
nodes fixed to each other OR internal mammary node metastasis
Metastasis = 0 No distant metastasis Metastasis = 1 Distant
metastasis Stage = 1 T1, N0, M0 Stage = 2 T0, N1, M0 T1, N1, M0 T2,
N0, M0 T2, N1, M0 T3, N0, M0 Stage = 3 T(0-3), N2, M0 T3, N1, M0
T4, NX, M0 Stage = 4 TX, NX, M1
[0241] Samples from patients in the cohort were stained with
antibodies from the breast cancer classification panel identified
in Appendix A (as previously described in Examples 2 and 3). The
stained samples were then scored in a semi-quantitative fashion,
with 0=negative, 1=weak staining, and 2=strong staining. When
appropriate, alternative scoring systems were used (i.e.,
0=negative, 1=weak or strong; or 0=negative or weak and 1=strong
staining). For each antibody, the scoring system used was selected
to produce the most significant prognostication of the patients, as
determined by a log-rank test (e.g., see Mantel and Haenszel,
Journal of the National Cancer Institute 22:719-748, 1959). The
results are presented in Appendix C and are grouped into four
categories that have been clinically recognized to be of
significance: all patients, ER+ patients, ER-- patients, and
ER+/lymph node metastases negative patients. As shown, the
antibodies were found to have differing significances for each of
these categories of breast cancer patients.
[0242] It is to be understood that exclusion of a particular
antibody from any prognostic panel based on these experiments is
not determinative. Indeed, it is anticipated that additional data
with other samples may lead to the identification of other
antibodies (from Appendix A and beyond) that may have prognostic
value for these and other classes of patients.
[0243] The expected relationship between the staining of patient
samples with each antibody and the recurrence of tumors was
measured using the Kaplan-Meier estimate of expected recurrence
(e.g., see Kaplan and Meier, J. Am. Stat. Assn. 53:457-81, 1958).
The log-rank test was used to determine the significance of
different expected recurrences for each antibody (e.g., see Mantel
and Haenszel, Journal of the National Cancer Institute, 22:719-748,
1959). This produces the p-value that is listed for each antibody
in Appendix C. Preferred antibodies are those that produce a
p-value of less than 0.10.
[0244] The degree to which these antibodies predicted recurrence
was determined using a Cox univariate proportional hazard model
(e.g., see Cox and Oakes, "Analysis of Survival Data", Chapman
& Hall, 1984). The "hazard ratio" listed in Appendix C for each
antibody reflects the predicted increase in risk of recurrence for
each increase in the staining score. Scores greater than 1.0
indicate that staining predicts an increased risk of recurrence
compared to an average individual, scores less than 1.0 indicate
that staining predicts a decreased risk.
[0245] It will be appreciated that these antibodies can be used
alone or in combinations to predict recurrence (e.g., in
combinations of 2, 3, 4, 5, 6, 7, 8, 9, 10 or more antibodies). It
will also be appreciated that while a given antibody may not
predict recurrence when used alone the same antibody may predict
recurrence when used in combination with others. It will also be
understood that while a given antibody or combination of antibodies
may not predict recurrence in a given set of patients (e.g., ER+
patients), the same antibody or combination of antibodies may
predict recurrence in a different set of patients (e.g., ER--
patients). Similarly, it is to be understood that while a given
antibody or combination of antibodies may not predict recurrence in
a given set of patients (e.g., ER+ patients), the same antibody or
combination of antibodies may predict recurrence in a subset of
these patients (e.g., ER+/node negative patients).
[0246] These prognostic panels could be constructed using any
method. Without limitation these include simple empirically derived
rules, Cox multivariate proportional hazard models (e.g., see Cox
and Oakes, "Analysis of Survival Data", Chapman & Hall, 1984),
regression trees (e.g., see Segal and Bloch, Stat. Med. 8:539-50,
1989), and/or neural networks (e.g., see Ravdin et al., Breast
Cancer Res. Treat. 21:47-53, 1992). In certain embodiments a
prognostic panel might include between 2-10 antibodies, for example
3-9 or 5-7 antibodies. It will be appreciated that these ranges are
exemplary and non-limiting.
[0247] The prognostic value of exemplary panels of antibodies were
also assessed by generating Kaplan-Meier recurrence curves for ER+
and ER+/lymph node metastases negative patients and then comparing
these with curves produced for these same patients with the
standard Nottingham Prognostic Index (NPI).
[0248] In order to generate Kaplan-Meier curves based on antibody
panels, Cox univariate proportional hazard regression models were
first run with all antibodies from Appendix C utilizing all three
scoring procedures. The antibodies and scoring systems best able to
predict recurrence were then used in a regression tree model and
pruned to maintain predictive power while reducing complexity.
Patients whom the model predicted as being strongly likely to recur
were placed in the "poor" prognosis group. Patients whom the model
predicted as being strongly unlikely to recur were given the
prediction of "good". Patients whom the model predicted as neither
being strongly likely to recur or not recur were placed in the
"moderate" prognosis group. Kaplan-Meier curves were then
calculated based on recurrence data for patients within each group.
FIG. 4A show the curves that were obtained for ER+ patients in each
of these prognostic groups. FIG. 5A show the curves that were
obtained for ER+/lymph node metastases negative patients in each of
these prognostic groups.
[0249] The antibodies from Appendix C that were used to predict
recurrence for ER+ patients (FIG. 4A) were: S0296P1 (1:225
dilution, scoring method 3), S6006 (1:1 dilution, scoring method
2), S0545 (1:900 dilution, scoring method 2), S0063 (1:300
dilution, scoring method 2), S6002 (1:1 dilution, scoring method
3), S0081 (1:20 dilution, scoring method 2), S0255 (1:1000
dilution, scoring method 3), and S0039 (1:100 dilution, scoring
method 2).
[0250] The antibodies from Appendix C that were used to predict
recurrence for ER+/lymph node metastases negative patients (FIG.
5A) were: S0143P3 (1:630 dilution, scoring method 1), S0137 (1:2500
dilution, scoring method 2), S0260 (1:5400 dilution, scoring method
2), S0702 (1:178200 dilution, scoring method 2), S0545 (1:900
dilution, scoring method 2), S6002 (1:1 dilution, scoring method
1), S6007 (1:1 dilution, scoring method 1).
[0251] Kaplan-Meier recurrence curves were then generated for the
same patients based on their standard NPI scores. NPI scores were
calculated for patients according to the standard formula
NPI=(0.2.times. tumor diameter in cm)+lymph node stage+ tumor
grade. As is well known in the art, lymph node stage is either 1
(if there are no nodes affected), 2 (if 1-3 glands are affected) or
3 (if more than 3 glands are affected). The tumor grade was scored
according to the Bloom-Richardson Grade system (Bloom and
Richardson, Br. J. Cancer 11:359-377, 1957). According to this
system, tumors were examined histologically and given a score for
the frequency of cell mitosis (rate of cell division), tubule
formation (percentage of cancer composed of tubular structures),
and nuclear pleomorphism (change in cell size and uniformity). Each
of these features was assigned a score ranging from 1 to 3 as shown
in Table 4. The scores for each feature were then added together
for a final sum that ranged between 3 to 9. A tumor with a final
sum of 3, 4, or 5 was considered a Grade 1 tumor (less aggressive
appearance); a sum of 6 or 7 a Grade 2 tumor (intermediate
appearance); and a sum of 8 or 9 a Grade 3 tumor (more aggressive
appearance).
5 TABLE 4 Score Tubule formation (% of carcinoma composed of
tubular structures) >75% 1 10-75% 2 <10% 3 Nuclear
pleomorphism (Change in Cells) Small, uniform cells 1 Moderate
increase in size and 2 variation Marked variation 3 Mitosis Count
(Cell Division) Up to 7 1 8 to 14 2 15 or more 3
[0252] Patients with tumors having an overall NPI score of less
than 3.4 were placed in the "good" prognosis group. Those with an
NPI score of between 3.4 and 5.4 were placed in the "moderate"
prognosis group and patients with an NPI score of more than 5.4
were placed in the "poor" prognosis group. Kaplan-Meier curves were
then calculated based on recurrence data for patients within each
group. FIG. 4B show the curves that were obtained for ER+ patients
in each of these NPI prognostic groups. FIG. 5B show the curves
that were obtained for ER+/lymph node metastases negative patients
in each of these NPI prognostic groups. By definition ER+/lymph
node metastases negative patients have an NPI score that is less
than 5.4. This explains why there is no "poor" prognosis curve in
FIG. 5B.
Example 11
Correlating Interaction Partner Binding With Clinical Prognostic
Data in Lung Cancer
[0253] This Example describes the identification of exemplary
panels of antibodies whose binding has been shown to correlate with
the prognosis of lung cancer patients. The data was obtained using
samples from the Huntsville Hospital lung cohort (the "HH lung"
cohort) that was referred to in Example 5.
[0254] The HH lung cohort was generated from 544 lung cancer
patients that were treated by the Comprehensive Cancer Institute
(Huntsville, Ala.) between 1987 and 2002. This larger group was
filtered to a study group of 379 patients by eliminating patients
that had insufficient clinical follow up or that did not have
sufficient clinical material in the paraffin block to sample. For
the remaining patients, clinical data through Sep. 30, 2003 was
available. This set of patients consisted of 232 males and 147
females. The average time of follow-up among patients who did not
recur was 3.5 years. Of the 379 patients, 103 had a recurrence of
cancer within the study period. All patients in this study were
diagnosed at a pathological stage of 1 or 2, with 305 patients at
stage 1, 1A, or 1B, and 74 patients at stage 2, 2A, or 2B.
[0255] Samples from patients in the cohort were stained with
antibodies from the lung cancer classification panel identified in
Appendix A (as previously described in Examples 4 and 5). The
stained samples were then scored in a semi-quantitative fashion;
scoring methods 1-3 use the following schemes: method 1
(0=negative; 1=weak; 2=strong); method 2 (0=negative; 1=weak or
strong); and method 3 (0=negative or weak; 1=strong). For each
antibody, the scoring system used was selected to produce the most
significant prognostication of the patients, as determined by a
log-rank test (e.g., see Mantel and Haenszel, Journal of the
National Cancer Institute 22:719-748, 1959). The results are
presented in Appendix D and are grouped into three categories that
have been clinically recognized to be of significance: all
patients, adenocarcinoma patients, and squamous cell carcinoma
patients. As shown, the antibodies were found to have differing
significances for each of these categories of lung cancer
patients.
[0256] It is to be understood that exclusion of a particular
antibody from any prognostic panel based on these experiments is
not determinative. Indeed, it is anticipated that additional data
with other samples may lead to the identification of other
antibodies (from Appendix A and beyond) that may have prognostic
value for these and other classes of patients.
[0257] As for the breast study of Example 11, the expected
relationship between the staining of patient samples with each
antibody and the recurrence of tumors was measured using the
Kaplan-Meier estimate of expected recurrence and a log-rank test
was used to determine the significance of different expected
recurrences. This produces the p-value that is listed for each
antibody in Appendix D. Preferred antibodies are those that produce
a p-value of less than 0.10.
[0258] The degree to which these antibodies predicted recurrence
was determined using a Cox univariate proportional hazard model.
The "hazard ratio" listed in Appendix D for each antibody reflects
the predicted increase in risk of recurrence for each increase in
the staining score. Scores greater than 1.0 indicate that staining
predicts an increased risk of recurrence compared to an average
individual, scores less than 1.0 indicate that staining predicts a
decreased risk.
[0259] As a number of patients had information regarding whether or
not the cancer recurred but lacked information on time to
recurrence, a chi-square test was also performed. This standard
statistical test shows the degree of divergence between observed
and expected frequencies and does not employ time to recurrence, as
does the log-rank test. Preferred antibodies are those that produce
a p-value of less than 0.10.
[0260] It will be appreciated that these prognostic antibodies can
be used alone or in combinations to predict recurrence (e.g., in
combinations of 2, 3, 4, 5, 6, 7, 8, 9, 10 or more antibodies). It
will also be appreciated that while a given antibody may not
predict recurrence when used alone, the same antibody may predict
recurrence when used in combination with others. It will also be
understood that while a given antibody or combination of antibodies
may not predict recurrence in a given set of patients (e.g.,
adenocarcinoma patients), the same antibody or combination of
antibodies may predict recurrence in a different set of patients
(e.g., squamous cell carcinoma patients).
[0261] As for the breast study of Example 11, these prognostic
panels could be constructed using any method. Without limitation
these include simple empirically derived rules, Cox multivariate
proportional hazard models, regression trees, and/or neural
networks. In certain embodiments a prognostic panel might include
between 2-10 antibodies, for example 3-9 or 5-7 antibodies. It will
be appreciated that these ranges are exemplary and
non-limiting.
Other Embodiments
[0262] Other embodiments of the invention will be apparent to those
skilled in the art from a consideration of the specification or
practice of the invention disclosed herein. It is intended that the
specification and examples be considered as exemplary only, with
the true scope of the invention being indicated by the following
claims.
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